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2. Introduction to a Thematic Set of Papers on Remote Sensing for Natural Hazards Assessment and Control
- Author
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Paolo Mazzanti and Saverio Romeo
- Subjects
remote sensing ,natural hazards ,hazard ,vulnerability ,risk assessment ,Science - Abstract
Remote sensing is currently showing high potential to provide valuable information at various spatial and temporal scales concerning natural hazards and their associated risks. Recent advances in technology and processing methods have strongly contributed to the development of disaster risk reduction research. In this Special Issue titled “Remote Sensing for Natural Hazards Assessment and Control”, we propose state-of-the-art research that specifically addresses multiple aspects of the use of remote sensing for natural hazards. The aim was to collect innovative methodologies, expertise, and capabilities to detect, assess monitor, and model natural hazards. In this regard, 18 open-access papers showcase scientific studies based on the exploitation of a broad range of remote sensing data and techniques, as well as focusing on a well-assorted sample of natural hazard types.
- Published
- 2023
- Full Text
- View/download PDF
3. Scientometric Full-Text Analysis of Papers Published in Remote Sensing between 2009 and 2021
- Author
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Timo Balz
- Subjects
scientometric ,remote sensing ,trends ,cooperation ,readability ,Science - Abstract
Covering the full texts of all papers published in MDPI’s Remote Sensing between 2009 and 2021, in-depth scientometric analyses were conducted. Trends in publications show an increase in the overall number of papers. A relative increase in papers using SAR sensors and a relative decrease in papers using optical remote sensing can also be seen. The full-text analyses reveal distinctive styles and writing patterns for papers from different sub-fields of remote sensing and for different countries and even cities. While a slight increase in the readability of abstracts is detected over time, the overall readability of papers is decreasing. Institutional co-authorship analysis reveals the ongoing ‘scientific decoupling’ between China and the USA in remote sensing. Using scientometric full-text analysis, current trends and developments are revealed.
- Published
- 2022
- Full Text
- View/download PDF
4. Introduction to a Thematic Set of Papers on Remote Sensing for Natural Hazards Assessment and Control.
- Author
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Mazzanti, Paolo and Romeo, Saverio
- Subjects
- *
REMOTE sensing , *RISK assessment - Abstract
Remote sensing is currently showing high potential to provide valuable information at various spatial and temporal scales concerning natural hazards and their associated risks. Recent advances in technology and processing methods have strongly contributed to the development of disaster risk reduction research. In this Special Issue titled "Remote Sensing for Natural Hazards Assessment and Control", we propose state-of-the-art research that specifically addresses multiple aspects of the use of remote sensing for natural hazards. The aim was to collect innovative methodologies, expertise, and capabilities to detect, assess monitor, and model natural hazards. In this regard, 18 open-access papers showcase scientific studies based on the exploitation of a broad range of remote sensing data and techniques, as well as focusing on a well-assorted sample of natural hazard types. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
5. Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers
- Author
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Teo Nguyen, Benoît Liquet, Kerrie Mengersen, and Damien Sous
- Subjects
coral mapping ,coral reefs ,machine learning ,remote sensing ,satellite imagery ,Science - Abstract
Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.
- Published
- 2021
- Full Text
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6. Polarimetric Synthetic Aperture Radar Speckle Filter Based on Joint Similarity Measurement Criterion.
- Author
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Tang, Fanyi, Li, Zhenfang, Zhang, Qingjun, Suo, Zhiyong, Zhang, Zexi, Xing, Chao, and Guo, Huancheng
- Subjects
SYNTHETIC aperture radar ,POLARIMETRY ,SYNTHETIC apertures ,SPECKLE interference ,ADAPTIVE filters ,FILTER paper - Abstract
Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target interpretation. With the rapid increase in PolSAR resolution, SAR images in complex natural and artificial scenes exhibit non-homogeneous characteristics, which creates an urgent demand for high-resolution PolSAR filters. To address these issues, a new adaptive PolSAR filter based on joint similarity measure criterion (JSMC) is proposed in this paper. Firstly, a scale-adaptive filtering window is established in order to preserve the texture structure based on a multi-directional ratio edge detector. Secondly, the JSMC is proposed in order to accurately select homogeneous pixels; it describes pixel similarity based on both space distance and polarimetric distance. Thirdly, the homogeneous pixels are filtered based on statistical averaging. Finally, the airborne and spaceborne real data experiment results validate the effectiveness of our proposed method. Compared with other filters, the filter proposed in this paper provides a better outcome for PolSAR data in speckle suppression, edge texture, and the preservation of polarimetric properties. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
7. Scientometric Full-Text Analysis of Papers Published in Remote Sensing between 2009 and 2021.
- Author
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Balz, Timo
- Subjects
- *
REMOTE sensing , *TEXT files , *OPTICAL remote sensing - Abstract
Covering the full texts of all papers published in MDPI's Remote Sensing between 2009 and 2021, in-depth scientometric analyses were conducted. Trends in publications show an increase in the overall number of papers. A relative increase in papers using SAR sensors and a relative decrease in papers using optical remote sensing can also be seen. The full-text analyses reveal distinctive styles and writing patterns for papers from different sub-fields of remote sensing and for different countries and even cities. While a slight increase in the readability of abstracts is detected over time, the overall readability of papers is decreasing. Institutional co-authorship analysis reveals the ongoing 'scientific decoupling' between China and the USA in remote sensing. Using scientometric full-text analysis, current trends and developments are revealed. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
8. Editorial of Special Issue "Remote Sensing Observations to Improve Knowledge of Lithosphere–Atmosphere–Ionosphere Coupling during the Preparatory Phase of Earthquakes".
- Author
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Marchetti, Dedalo, Yuan, Yunbin, and Zhu, Kaiguang
- Subjects
REMOTE sensing ,EARTHQUAKES ,NEPAL Earthquake, 2015 ,GEOMAGNETISM ,KAHRAMANMARAS Earthquake, Turkey & Syria, 2023 ,EARTHQUAKE magnitude ,SEISMIC tomography - Abstract
This document is an editorial for a special issue of the journal Remote Sensing, which focuses on using satellite data and new methodologies to understand the preparatory phase of medium-large earthquakes. The issue includes 15 papers from authors in various countries, covering topics such as seismo-electromagnetic processes, lithospheric structure, atmospheric anomalies, ionospheric disturbances, and interactions between the lithosphere, atmosphere, and ionosphere. The editorial emphasizes the need for further research to explain the different patterns observed in earthquakes and the potential role of tectonic settings and water in these phenomena. Additionally, there is an acknowledgment section from a research paper published in the journal, expressing gratitude to the academic editors who helped evaluate the papers in the special issue. [Extracted from the article]
- Published
- 2024
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9. Self-Adaptive Filtering for Ultra-Large-Scale Airborne LiDAR Data in Urban Environments Based on Object Primitive Global Energy Minimization.
- Author
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Hui, Zhenyang, Li, Zhuoxuan, Li, Dajun, Xu, Yanan, and Wang, Yuqian
- Subjects
LIDAR ,SELF-adaptive software ,SMART cities ,ENERGY function ,FILTER paper ,SERVER farms (Computer network management) - Abstract
Filtering from airborne LiDAR datasets in urban area is one important process during the building of digital and smart cities. However, the existing filters encounter poor filtering performance and heavy computational burden when processing large-scale and complicated urban environments. To tackle this issue, a self-adaptive filtering method based on object primitive global energy minimization is proposed in this paper. In this paper, mode points were first acquired for generating the mode graph. The mode points were the cluster centers of the LiDAR data obtained in a mean shift algorithm. The graph constructed with mode points was named "mode graph" in this paper. By defining the energy function based on the mode graph, the filtering process is transformed to iterative global energy minimization. In each iteration, the graph cuts technique was adopted to achieve global energy minimization. Meanwhile, the probability of each point belonging to the ground was updated, which would lead to a new refined ground surface using the points whose probabilities were greater than 0.5. This process was iterated until two successive fitted ground surfaces were determined to be close enough. Four urban samples with different urban environments were adopted for verifying the effectiveness of the filter developed in this paper. Experimental results indicate that the developed filter obtained the best filtering performance. Both the total error and the Kappa coefficient are superior to those of the other three classical filtering methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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10. Microwave and Radar Week (MRW 2020): Selected Papers
- Author
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Konrad Jędrzejewski, Paolo Colantonio, and Adam Abramowicz
- Subjects
n/a ,Science - Abstract
The 9th Microwave and Radar Week (MRW 2020) was held in Warsaw the capital of Poland, on 5–7 October 2020 [...]
- Published
- 2021
- Full Text
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11. Editorial for the Special Issue on Selected Papers from the '2019 International Symposium on Remote Sensing'
- Author
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Fuan Tsai, Chao-Hung Lin, Walter W. Chen, Jen-Jer Jaw, and Kuo-Hsin Tseng
- Subjects
n/a ,Science - Abstract
The 2019 International Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications with geospatial sensors, systems, and data, in conjunction with emerging technologies such as artificial intelligence, machine leaning and advanced spatial analysis algorithms. The topics of the selected papers include the following: the on-orbit radiometric calibration of satellite optical sensors, environmental characteristics assessment with remote sensing, machine learning-based photogrammetry and image analysis, and the integration of remote sensing and spatial analysis. The selected contributions also demonstrate and discuss various sophisticated applications in utilizing remote sensing, geospatial data, and technologies to address different environmental and societal issues. Readers should find the Special Issue enlightening and insightful for understanding state-of-the-art remote sensing and spatial information science research, development and applications.
- Published
- 2020
- Full Text
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12. Special Issue on Selected Papers from "International Symposium on Remote Sensing 2021".
- Author
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Hong, Sang-Hoon, Kim, Jinsoo, and Jung, Hyung-Sup
- Subjects
- *
REMOTE sensing , *CONVOLUTIONAL neural networks , *NORMALIZED difference vegetation index ,KUROSHIO - Abstract
10.3390/rs13214334 7 Park S.-H., Yoo J., Son D., Kim J., Jung H.-S. Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network. Lee and Choi [[4]] proposed a daytime cloud detection algorithm using a multi-temporal Geostationary Korea Multi-Purpose Satellite 2A (GEO-KOMPSAT-2A, GK-2A) dataset. 10.3390/rs13214282 9 Park S.-H., Jung H.-S., Lee S., Kim E.-S. Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network. [Extracted from the article]
- Published
- 2023
- Full Text
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13. Remote Sensing Best Paper Award for the Year 2015
- Author
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Prasad S. Thenkabail
- Subjects
n/a ,Science - Abstract
As a follow-up to the Best Paper Award of 2014, recognizing the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing, we are pleased to announce the Remote Sensing Best Paper Award for the year 2015. [...]
- Published
- 2015
- Full Text
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14. Remote Sensing 10th Anniversary Best Paper Award
- Author
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Prasad S. Thenkabail
- Subjects
n/a ,Science - Abstract
Started in 2009, our journal will celebrate its 10th anniversary in 2019 [...]
- Published
- 2019
- Full Text
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15. Special Issue on Selected Papers from the 'International Symposium on Remote Sensing 2018'
- Author
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Hyung-Sup Jung, Joo-Hyung Ryu, Sang-Eun Park, Hoonyol Lee, and No-Wook Park
- Subjects
n/a ,Science - Abstract
The international symposium on remote sensing 2018 (ISRS 2018) was held in Pyeongchang, Korea, 9−11 May 2018 [...]
- Published
- 2019
- Full Text
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16. Computational Intelligence in Remote Sensing.
- Author
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Wu, Yue, Gong, Maoguo, Miao, Qiguang, and Qin, Kai
- Subjects
DEEP learning ,COMPUTATIONAL intelligence ,IMAGE recognition (Computer vision) ,OBJECT recognition (Computer vision) ,REMOTE sensing ,REMOTE-sensing images ,INTELLIGENT control systems ,DISTANCE education - Abstract
This document, titled "Computational Intelligence in Remote Sensing," discusses the application of computational intelligence (CI) methods in the field of remote sensing. It highlights recent research and progress in this area, categorizing the papers into four sections: computational intelligence methods in hyperspectral remote sensing images, object detection techniques in remote sensing images, deep learning approaches in remote sensing image classification, and intelligent optimization and control in satellite image applications. The document emphasizes the potential of CI in addressing the challenges of remote sensing and encourages further interdisciplinary cooperation to solve real-world problems. The authors express their gratitude to the contributors and highlight the achievements of the research papers in this journal. [Extracted from the article]
- Published
- 2023
- Full Text
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17. Determining Ionospheric Drift and Anisotropy of Irregularities from LOFAR Core Measurements: Testing Hypotheses behind Estimation.
- Author
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Grzesiak, Marcin, Pożoga, Mariusz, Matyjasiak, Barbara, Przepiórka, Dorota, Beser, Katarzyna, Tomasik, Lukasz, Rothkaehl, Hanna, and Ciechowska, Helena
- Subjects
ANISOTROPY ,DIFFRACTION patterns ,CONFERENCE papers ,STATISTICAL correlation ,SIGNAL processing - Abstract
We try to assess the validity of assumptions taken when deriving drift velocity. We give simple formulas for characteristics of the spatiotemporal correlation function of the observed diffraction pattern for the frozen flow and the more general Briggs model. Using Low-Frequency Array (LOFAR) Cassiopeia intensity observation, we compare the experimental velocity scaling factor with a theoretical one to show that both models do not follow observations. We also give a qualitative comparison of our drift velocity estimates with SuperDARN convection maps. The article is essentially an extended version of the conference paper: "Determining ionospheric drift and anisotropy of irregularities from LOFAR core measurements", Signal Processing Symposium 2021 (SPSympo 2021). [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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18. Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II.
- Author
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Jeon, Gwanggil
- Subjects
REMOTE sensing ,MACHINE learning ,ARTIFICIAL neural networks ,DEEP learning ,ARTIFICIAL intelligence ,DISTANCE education - Abstract
This document is a summary of a special issue on advanced machine learning and deep learning techniques for remote sensing. The issue includes 16 research papers that cover a range of topics, including hyperspectral image classification, moving point target detection, radar echo extrapolation, and remote sensing object detection. Each paper introduces a novel approach or model and provides extensive testing and evaluation to demonstrate its effectiveness. The insights shared in this special issue are expected to contribute to future advancements in artificial intelligence-based remote sensing research. [Extracted from the article]
- Published
- 2024
- Full Text
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19. Remote Sensing of Forests in Bavaria: A Review.
- Author
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Coleman, Kjirsten, Müller, Jörg, and Kuenzer, Claudia
- Subjects
REMOTE sensing ,BARK beetles ,FOREST monitoring ,FOREST management ,FOREST reserves ,SPACE-based radar ,PLANT phenology ,DROUGHTS - Abstract
In recent decades, climatic pressures have altered the forested landscape of Bavaria. Widespread loss of trees has unevenly impacted the entire state, of which 37% is covered by forests (5% more than the national average). In 2018 and 2019—due in large part to drought and subsequent insect infestations—more tree-covered areas were lost in Bavaria than in any other German state. Moreover, the annual crown condition survey of Bavaria has revealed a decreasing trend in tree vitality since 1998. We conducted a systematic literature review regarding the remote sensing of forests in Bavaria. In total, 146 scientific articles were published between 2008 and 2023. While 88 studies took place in the Bavarian Forest National Park, only five publications covered the whole of Bavaria. Outside of the national park, the remaining 2.5 million hectares of forest in Bavaria are understudied. The most commonly studied topics were related to bark beetle infestations (24 papers); however, few papers focused on the drivers of infestations. The majority of studies utilized airborne data, while publications utilizing spaceborne data focused on multispectral; other data types were under-utilized- particularly thermal, lidar, and hyperspectral. We recommend future studies to both spatially broaden investigations to the state or national scale and to increase temporal data acquisitions together with contemporaneous in situ data. Especially in understudied topics regarding forest response to climate, catastrophic disturbances, regrowth and species composition, phenological timing, and in the sector of forest management. The utilization of remote sensing data in the forestry sector and the uptake of scientific results among stakeholders remains a challenge compared to other heavily forested European countries. An integral part of the Bavarian economy and the tourism sector, forests are also vital for climate regulation via atmospheric carbon reduction and land surface cooling. Therefore, forest monitoring remains centrally important to attaining more resilient and productive forests. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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20. A Complex Background SAR Ship Target Detection Method Based on Fusion Tensor and Cross-Domain Adversarial Learning.
- Author
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Chan, Haopeng, Qiu, Xiaolan, Gao, Xin, and Lu, Dongdong
- Subjects
SYNTHETIC aperture radar ,RADAR targets ,LEARNING modules ,GENERALIZATION ,SHIPS - Abstract
Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effectively. This presents a cross-domain detection problem that requires further study. This paper proposes a method for detecting SAR ships in complex backgrounds using fusion tensor and cross-domain adversarial learning. The method is designed to address the cross-domain detection problem of SAR ships with large differences between the training and test sets. Specifically, it can be used for the cross-domain detection task from the fully polarised medium-resolution ship dataset (source domain) to the high-resolution single-polarised dataset (target domain). This method proposes a channel fusion module (CFM) based on the YOLOV5s model. The CFM utilises the correlation between polarised channel images during training to enrich the feature information of single-polarised images extracted by the model during inference. This article proposes a module called the cross-domain adversarial learning module (CALM) to reduce overfitting and achieve adaptation between domains. Additionally, this paper introduces the anti-interference head (AIH) which decouples the detection head to reduce the conflict of classification and localisation problems. This improves the anti-interference and generalisation ability in complex backgrounds. This paper conducts cross-domain experiments using the constructed medium-resolution SAR full polarisation dataset (SFPD) as the source domain and the high-resolution single-polarised ship detection dataset (HRSID) as the target domain. Compared to the best-performing YOLOV8s model among typical mainstream models, this model improves precision by 4.9%, recall by 3.3%, AP by 2.4%, and F1 by 3.9%. This verifies the effectiveness of the method and provides a useful reference for improving cross-domain learning and model generalisation capability in the field of target detection. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
21. Remote Sensing and Landsystems in the Mountain Domain: FAIR Data Accessibility and Landform Identification in the Digital Earth.
- Author
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Whalley, W. Brian
- Subjects
GLACIAL melting ,LANDFORMS ,REMOTE-sensing images ,ROCK concerts ,REMOTE sensing ,ROCK glaciers - Abstract
Satellite imagery has become a major source for identifying and mapping terrestrial and planetary landforms. However, interpretating landforms and their significance, especially in changing environments, may still be questionable. Consequently, ground truth to check training models, especially in mountainous areas, can be problematic. This paper outlines a decimal format, [dLL], for latitude and longitude geolocation that can be used for model interpretation and validation and in data sets. As data have positions in space and time, [dLL] defined points, as for images, can be associated with metadata as nodes. Together with vertices, metadata nodes help build 'information surfaces' as part of the Digital Earth. This paper examines aspects of the Critical Zone and data integration via the FAIR data principles, data that are; findable, accessible, interoperable and re-usable. Mapping and making inventories of rock glacier landforms are examined in the context of their geomorphic and environmental significance and the need for geolocated ground truth. Terrestrial examination of rock glaciers shows them to be predominantly glacier-derived landforms and not indicators of permafrost. Remote-sensing technologies used to track developing rock glacier surface features show them to be climatically melting glaciers beneath rock debris covers. Distinguishing between glaciers, debris-covered glaciers and rock glaciers over time is a challenge for new remote sensing satellites and technologies and shows the necessity for a common geolocation format to report many Earth surface features. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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22. Efficient Path Planning Algorithm Based on Laser SLAM and an Optimized Visibility Graph for Robots.
- Author
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Hu, Yunjie, Xie, Fei, Yang, Jiquan, Zhao, Jing, Mao, Qi, Zhao, Fei, and Liu, Xixiang
- Subjects
MOBILE robots ,GRIDS (Cartography) ,POINT cloud ,SEARCH algorithms ,SCHEDULING ,POTENTIAL field method (Robotics) - Abstract
Mobile robots' efficient path planning has long been a challenging task due to the complexity and dynamism of environments. If an occupancy grid map is used in path planning, the number of grids is determined by grid resolution and the size of the actual environment. Excessively high resolution increases the number of traversed grid nodes and thus prolongs path planning time. To address this challenge, this paper proposes an efficient path planning algorithm based on laser SLAM and an optimized visibility graph for mobile robots, which achieves faster computation of the shortest path using the optimized visibility graph. Firstly, the laser SLAM algorithm is used to acquire the undistorted LiDAR point cloud data, which are converted into a visibility graph. Secondly, a bidirectional A* path search algorithm is combined with the Minimal Construct algorithm, enabling the robot to only compute heuristic paths to the target node during path planning in order to reduce search time. Thirdly, a filtering method based on edge length and the number of vertices of obstacles is proposed to reduce redundant vertices and edges in the visibility graph. Additionally, the bidirectional A* search method is implemented for pathfinding in the efficient path planning algorithm proposed in this paper to reduce unnecessary space searches. Finally, simulation and field tests are conducted to validate the algorithm and compare its performance with classic algorithms. The test results indicate that the method proposed in this paper exhibits superior performance in terms of path search time, navigation time, and distance compared to D* Lite, FAR, and FPS algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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23. Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review.
- Author
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de França e Silva, Nildson Rodrigues, Chaves, Michel Eustáquio Dantas, Luciano, Ana Cláudia dos Santos, Sanches, Ieda Del'Arco, de Almeida, Cláudia Maria, and Adami, Marcos
- Subjects
REMOTE sensing ,SCIENCE databases ,SUGARCANE ,SUGARCANE growing ,DECISION making ,SUPPLY chains ,TEXT mining - Abstract
The sugarcane crop has great socioeconomic relevance because of its use in the production of sugar, bioelectricity, and ethanol. Mainly cultivated in tropical and subtropical countries, such as Brazil, India, and China, this crop presented a global harvested area of 17.4 million hectares (Mha) in 2021. Thus, decision making in this activity needs reliable information. Obtaining accurate sugarcane yield estimates is challenging, and in this sense, it is important to reduce uncertainties. Currently, it can be estimated by empirical or mechanistic approaches. However, the model's peculiarities vary according to the availability of data and the spatial scale. Here, we present a systematic review to discuss state-of-the-art sugarcane yield estimation approaches using remote sensing and crop simulation models. We consulted 1398 papers, and we focused on 72 of them, published between January 2017 and June 2023 in the main scientific databases (e.g., AGORA-FAO, Google Scholar, Nature, MDPI, among others), using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We observed how the models vary in space and time, presenting the potential, challenges, limitations, and outlooks for enhancing decision making in the sugarcane crop supply chain. We concluded that remote sensing data assimilation both in mechanistic and empirical models is promising and will be enhanced in the coming years, due to the increasing availability of free Earth observation data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
24. 3D Reconstruction of Ancient Buildings Using UAV Images and Neural Radiation Field with Depth Supervision.
- Author
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Ge, Yingwei, Guo, Bingxuan, Zha, Peishuai, Jiang, San, Jiang, Ziyu, and Li, Demin
- Subjects
BUILDING repair ,RADIATION ,SIGNAL-to-noise ratio ,POINT cloud ,DATA visualization - Abstract
The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures of ancient structures and geometric issues caused by repeated textures. Additionally, there is a hash conflict problem when rendering outdoor scenes using neural radiation fields. To address these challenges, this paper proposes a 3D reconstruction method based on depth-supervised neural radiation fields. To enhance the representation of the geometric neural network, the addition of a truncated signed distance function (TSDF) supplements the existing signed distance function (SDF). Furthermore, the neural network's training is supervised using depth information, leading to improved geometric accuracy in the reconstruction model through depth data obtained from sparse point clouds. This study also introduces a progressive training strategy to mitigate hash conflicts, allowing the hash table to express important details more effectively while reducing feature overlap. The experimental results demonstrate that our method, under the same number of iterations, produces images with clearer structural details, resulting in an average 15% increase in the Peak Signal-to-Noise Ratio (PSNR) value and a 10% increase in the Structural Similarity Index Measure (SSIM) value. Moreover, our reconstruction model produces higher-quality surface models, enabling the fast and highly geometrically accurate 3D reconstruction of ancient buildings. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. Editorial for Special Issue "Advances in Hyperspectral Data Exploitation".
- Author
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Chang, Chein-I, Song, Meiping, Yu, Chunyan, Wang, Yulei, Yu, Haoyang, Li, Jiaojiao, Wang, Lin, Li, Hsiao-Chi, and Li, Xiaorun
- Subjects
REMOTE sensing ,INFRARED imaging ,MULTISPECTRAL imaging - Abstract
Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data. In particular, its capability has expanded from unmixing data samples and detecting targets at the subpixel scale to finding endmembers, which generally cannot be resolved by multispectral imaging. Accordingly, a wealth of new HSI research has been conducted and reported in the literature in recent years. The aim of this Special Issue "Advances in Hyperspectral Data Exploitation" is to provide a forum for scholars and researchers to publish and share their research ideas and findings to facilitate the utility of hyperspectral imaging in data exploitation and other applications. With this in mind, this Special Issue accepted and published 19 papers in various areas, which can be organized into 9 categories, including I: Hyperspectral Image Classification, II: Hyperspectral Target Detection, III: Hyperspectral and Multispectral Fusion, IV: Mid-wave Infrared Hyperspectral Imaging, V: Hyperspectral Unmixing, VI: Hyperspectral Sensor Hardware Design, VII: Hyperspectral Reconstruction, VIII: Hyperspectral Visualization, and IX: Applications. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
26. Active Wildland Fires in Central Chile and Local Winds (Puelche).
- Author
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Hayasaka, Hiroshi
- Subjects
METEOROLOGICAL charts ,FIRE weather ,METEOROLOGICAL stations ,JET streams ,WEATHER - Abstract
Central Chile (CC, latitudes 32–40°S) experienced very active fires in 2017 and 2023. These fires burned large areas and killed many people. These unprecedented fires for CC presented a need for more defined fire weather conditions on the synoptic scale. In this paper, fire weather conditions were analyzed using various satellite-derived fire data (hotspots, HSs), wind streamlines, distribution maps of wind flow and temperature, and various synoptic-scale weather maps. Results showed that local winds, known as Puelche, blew on the peak fire days (26 January 2017 and 3 February 2023). The number of HSs on these days was 2676 and 2746, respectively, about 90 times the average (30). The occurrence of Puelche winds was confirmed by streamlines from high-pressure systems offshore of Argentina to the study area in CC. The formation of strong winds and high-temperature areas associated with Puelche winds were identified on the Earth survey satellite maps. Strong winds of about 38 km h
−1 and high temperatures above 32 °C with low relative humidity below 33% were actually observed at the weather station near the fire-prone areas. Lastly, some indications for Puelche winds outbreaks are summarized. This paper's results will be used to prevent future active fire occurrences in the CC. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
27. Multiple-Band Electric Field Response to the Geomagnetic Storm on 4 November 2021.
- Author
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Zheng, Jie, Huang, Jianping, Li, Zhong, Li, Wenjing, Han, Ying, Lu, Hengxin, and Zhima, Zeren
- Subjects
MAGNETIC storms ,GEOMAGNETISM ,ELECTRIC fields ,SPACE vehicles - Abstract
This paper investigates the impact characteristics of the 4 November 2021 magnetic storm across different frequency bands based on the electric field data (EFD) from the China Seismo-Electromagnetic Satellite (CSES), categorized into four frequency bands: ULF (Ultra-Low-Frequency, DC to 16 Hz), ELF (Extremely Low-Frequency, 6 Hz to 2.2 kHz), VLF (Very Low-Frequency, 1.8 to 20 kHz), and HF (High-Frequency, 18 kHz to 3.5 MHz). The study reveals that in the ULF band, magnetic storm-induced electric field disturbances are primarily in the range of 0 to 5 Hz, with a significant disturbance frequency at 3.9 ± 1.0 Hz. Magnetic storms also enhance Schumann waves in the ULF band, with 8 Hz Schumann waves dominating in the southern hemisphere and 13 Hz Schumann waves dominating in the northern hemisphere. In the ELF band, the more pronounced anomalies occur at 300 Hz–900 Hz and above 1.8 kHz, with the 300 Hz–900 Hz band anomalies around 780 Hz being the most significant. In the VLF band, the electric field anomalies are mainly concentrated in the 3–15 kHz range. The ELF and VLF bands exhibit lower absolute and relative disturbance increments compared to the ULF band, with the relative perturbation growth rate in the ULF band being approximately 10% higher than in the ELF and VLF bands. Magnetic storm-induced electric field disturbances predominantly occur in the ULF, ELF, and VLF bands, with the most significant disturbances in the ULF band. The electric field perturbations in these three frequency bands exhibit hemispheric asymmetry, with strong perturbations in the northern hemisphere occurring earlier than in the southern hemisphere, corresponding to different Dst minima. No electric field disturbances were observed in the HF band (above 18 kHz). The conclusions of this paper are highly significant for future anti-jamming designs in spacecraft and communication equipment, as well as for the further study of magnetic storms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. Combining KAN with CNN: KonvNeXt's Performance in Remote Sensing and Patent Insights.
- Author
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Cheon, Minjong and Mun, Changbae
- Subjects
REMOTE sensing ,MACHINE learning ,DEEP learning ,WEATHER forecasting ,DISTANCE education - Abstract
Rapid advancements in satellite technology have led to a significant increase in high-resolution remote sensing (RS) images, necessitating the use of advanced processing methods. Additionally, patent analysis revealed a substantial increase in deep learning and machine learning applications in remote sensing, highlighting the growing importance of these technologies. Therefore, this paper introduces the Kolmogorov-Arnold Network (KAN) model to remote sensing to enhance efficiency and performance in RS applications. We conducted several experiments to validate KAN's applicability, starting with the EuroSAT dataset, where we combined the KAN layer with multiple pre-trained CNN models. Optimal performance was achieved using ConvNeXt, leading to the development of the KonvNeXt model. KonvNeXt was evaluated on the Optimal-31, AID, and Merced datasets for validation and achieved accuracies of 90.59%, 94.1%, and 98.1%, respectively. The model also showed fast processing speed, with the Optimal-31 and Merced datasets completed in 107.63 s each, while the bigger and more complicated AID dataset took 545.91 s. This result is meaningful since it achieved faster speeds and comparable accuracy compared to the existing study, which utilized VIT and proved KonvNeXt's applicability for remote sensing classification tasks. Furthermore, we investigated the model's interpretability by utilizing Occlusion Sensitivity, and by displaying the influential regions, we validated its potential use in a variety of domains, including medical imaging and weather forecasting. This paper is meaningful in that it is the first to use KAN in remote sensing classification, proving its adaptability and efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Compensating Acquisition Footprint for Amplitude-Preserving Angle Domain Common Image Gathers Based on 3D Reverse Time Migration.
- Author
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Liu, Hongwei, Fu, Liyun, Li, Qingqing, and Liu, Lu
- Subjects
LINEAR velocity ,SEISMIC prospecting ,ANGLES ,LIGHTING ,VELOCITY - Abstract
Angle domain common image gathers (ADCIGs) play a crucial role in seismic exploration, offering prestack underground illumination information that aids in validating migration velocity and conducting prestack amplitude versus angle (AVA) analysis for reservoir characterization. This paper introduces an innovative approach for compensating amplitude errors caused by irregular seismic acquisition geometries in ADCIGs. By incorporating an angle domain illumination compensation factor, the proposed method effectively modifies these errors, preserving the amplitude of seismic reflectivity in the prestack angle domain. The effectiveness of the proposed approach is validated through comprehensive tests conducted on synthetic and field data examples. The results demonstrate the capability of the method to enhance the quality of ADCIGs derived from 3D reverse time migration (RTM), yielding accurate and reliable amplitude preservation. While the illumination compensation factor assumes a vertically linear velocity model, the method holds promise for extension to more complex media and diverse migration techniques. This suggests its applicability and adaptability beyond the specific assumptions considered in this study. In conclusion, this paper presents an innovative angle domain illumination compensation factor that significantly improves the quality of ADCIGs by addressing amplitude errors arising from irregular seismic acquisition geometries. The experimental validation using synthetic and field data confirms the effectiveness of the proposed method within the context of 3D RTM. Furthermore, the technique holds potential for broader application in more complex subsurface scenarios and various migration methodologies. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
30. Intelligent Tracking Method for Aerial Maneuvering Target Based on Unscented Kalman Filter.
- Author
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Dong, Yunlong, Li, Weiqi, Li, Dongxue, Liu, Chao, and Xue, Wei
- Subjects
TRACKING algorithms ,DESIGN techniques ,DYNAMIC models ,REGRESSION analysis ,RECURRENT neural networks - Abstract
This paper constructs a nonlinear iterative filtering framework based on a neural network prediction model. It uses recurrent neural networks (RNNs) to achieve accurate regression of complex maneuvering target dynamic models and integrates them into the nonlinear iterative filtering system via Unscented Transformation (UT). In constructing the neural network prediction model, the Temporal Convolutional Network (TCN) modules that capture long-term dependencies and the Long Short-Term Memory (LSTM) modules that selectively forget non-essential information were utilized to achieve accurate regression of the maneuvering models. When embedding the neural network prediction model, this paper proposes a method for extracting Sigma points using the UT transformation by 'unfolding' multi-sequence vectors and explores design techniques for the time sliding window length of recurrent neural networks. Ultimately, an intelligent tracking algorithm based on unscented filtering, called TCN-LSTM-UKF, was developed, effectively addressing the difficulties of constructing models and transition delays under high-maneuvering conditions and significantly improving the tracking performance of highly maneuvering targets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration.
- Author
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Li, Tong, Cui, Lizhen, Wu, Yu, McLaren, Timothy I., Xia, Anquan, Pandey, Rajiv, Liu, Hongdou, Wang, Weijin, Xu, Zhihong, Song, Xiufang, Dalal, Ram C., and Dang, Yash P.
- Subjects
NATURAL language processing ,CARBON cycle ,SYNTHETIC aperture radar ,AGRICULTURE ,CARBON sequestration ,SYNTHETIC apertures - Abstract
Understanding and monitoring soil organic carbon (SOC) stocks is crucial for ecosystem carbon cycling, services, and addressing global environmental challenges. This study employs the BERTopic model and bibliometric trend analysis exploration to comprehensively analyze global SOC estimates. BERTopic, a topic modeling technique based on BERT (bidirectional encoder representatives from transformers), integrates recent advances in natural language processing. The research analyzed 1761 papers on SOC and remote sensing (RS), in addition to 490 related papers on machine learning (ML) techniques. BERTopic modeling identified nine research themes for SOC estimation using RS, emphasizing spectral prediction models, carbon cycle dynamics, and agricultural impacts on SOC. In contrast, for the literature on RS and ML it identified five thematic clusters: spatial forestry analysis, hyperspectral soil analysis, agricultural deep learning, the multitemporal imaging of farmland SOC, and RS platforms (Sentinel-2 and synthetic aperture radar, SAR). From 1991 to 2023, research on SOC estimation using RS and ML has evolved from basic mapping to topics like carbon sequestration and modeling with Sentinel-2A and big data. In summary, this study traces the historical growth and thematic evolution of SOC research, identifying synergies between RS and ML and focusing on SOC estimation with advanced ML techniques. These findings are critical to global ecosystem SOC assessments and environmental policy formulation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. SPNet: Dual-Branch Network with Spatial Supplementary Information for Building and Water Segmentation of Remote Sensing Images.
- Author
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Zhao, Wenyu, Xia, Min, Weng, Liguo, Hu, Kai, Lin, Haifeng, Zhang, Youke, and Liu, Ziheng
- Subjects
REMOTE sensing ,FEATURE extraction ,URBAN planning ,LAND use planning ,GOAL (Psychology) - Abstract
Semantic segmentation is primarily employed to generate accurate prediction labels for each pixel of the input image, and then classify the images according to the generated labels. Semantic segmentation of building and water in remote sensing images helps us to conduct reasonable land planning for a city. However, many current mature networks face challenges in simultaneously attending to both contextual and spatial information when performing semantic segmentation on remote sensing imagery. This often leads to misclassifications and omissions. Therefore, this paper proposes a Dual-Branch Network with Spatial Supplementary Information (SPNet) to address the aforementioned issues. We introduce a Context-aware Spatial Feature-Extractor Unit (CSF) to extract contextual and spatial information, followed by the Feature-Interaction Module (FIM) to supplement contextual semantic information with spatial details. Additionally, incorporating the Goal-Oriented Attention Mechanism helps in handling noise. Finally, to obtain more detailed branches, a Multichannel Deep Feature-Extraction Module (MFM) is introduced to extract features from shallow-level network layers. This branch guides the fusion of low-level semantic information with high-level semantic information. Experiments were conducted on building and water datasets, respectively. The results indicate that the segmentation accuracy of the model proposed in this paper surpasses that of other existing mature models. On the building dataset, the mIoU reaches 87.57, while on the water dataset, the mIoU achieves 96.8, which means that the model introduced in this paper demonstrates strong generalization capabilities. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
33. Continuous Wavelet Transform Peak-Seeking Attention Mechanism Conventional Neural Network: A Lightweight Feature Extraction Network with Attention Mechanism Based on the Continuous Wave Transform Peak-Seeking Method for Aero-Engine Hot Jet Fourier Transform Infrared Classification
- Author
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Du, Shuhan, Han, Wei, Kang, Zhenping, Lu, Xiangning, Liao, Yurong, and Li, Zhaoming
- Subjects
FOURIER transform spectrometers ,WAVELET transforms ,FEATURE extraction ,DEEP learning ,WAVENUMBER ,RUNNING speed - Abstract
Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines were created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak-seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data are extracted by the statistics of the wave number locations with high frequency. The attention mechanism was used for the peak data, and the attention mechanism was weighted to the feature map of the feature extraction block. The training set, validation set and prediction set were divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, the CWT-AM-CNN proposed in this paper was compared with the classical classifier algorithm based on CO
2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets was as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets. [ABSTRACT FROM AUTHOR]- Published
- 2024
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- View/download PDF
34. Radiation Anomaly Detection of Sub-Band Optical Remote Sensing Images Based on Multiscale Deep Dynamic Fusion and Adaptive Optimization.
- Author
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Ci, Jinlong, Tan, Hai, Zhai, Haoran, and Tang, Xinming
- Subjects
TRANSFORMER models ,IMAGE sensors ,REMOTE sensing ,RECORDS management ,DATA transmission systems - Abstract
Radiation anomalies in optical remote sensing images frequently occur due to electronic issues within the image sensor or data transmission errors. These radiation anomalies can be categorized into several types, including CCD, StripeNoise, RandomCode1, RandomCode2, ImageMissing, and Tap. To ensure the retention of image data with minimal radiation issues as much as possible, this paper adopts a self-made radiation dataset and proposes a FlexVisionNet-YOLO network to detect radiation anomalies more accurately. Firstly, RepViT is used as the backbone network with a vision transformer architecture to better capture global and local features. Its multiscale feature fusion mechanism efficiently handles targets of different sizes and shapes, enhancing the detection ability for radiation anomalies. Secondly, a feature depth fusion network is proposed in the Feature Fusion part, which significantly improves the flexibility and accuracy of feature fusion and thus enhances the detection and classification performance of complex remote sensing images. Finally, Inner-CIoU is used in the Head part for edge regression, which significantly improves the localization accuracy by finely adjusting the target edges; Slide-Loss is used for classification loss, which enhances the classification robustness by dynamically adjusting the category probabilities and markedly improves the classification accuracy, especially in the sample imbalance dataset. Experimental results show that, compared to YOLOv8, the proposed FlexVisionNet-YOLO method improves precision, recall, mAP0.5, and mAP0.5:0.9 by 3.5%, 7.1%, 4.4%, and 13.6%, respectively. Its effectiveness in detecting radiation anomalies surpasses that of other models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. Leveraging Visual Language Model and Generative Diffusion Model for Zero-Shot SAR Target Recognition.
- Author
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Wang, Junyu, Sun, Hao, Tang, Tao, Sun, Yuli, He, Qishan, Lei, Lin, and Ji, Kefeng
- Subjects
LANGUAGE models ,OPTICAL remote sensing ,COMPUTATIONAL electromagnetics ,KNOWLEDGE base ,SYNTHETIC aperture radar ,PRIOR learning ,IMAGE recognition (Computer vision) - Abstract
Simulated data play an important role in SAR target recognition, particularly under zero-shot learning (ZSL) conditions caused by the lack of training samples. The traditional SAR simulation method is based on manually constructing target 3D models for electromagnetic simulation, which is costly and limited by the target's prior knowledge base. Also, the unavoidable discrepancy between simulated SAR and measured SAR makes the traditional simulation method more limited for target recognition. This paper proposes an innovative SAR simulation method based on a visual language model and generative diffusion model by extracting target semantic information from optical remote sensing images and transforming it into a 3D model for SAR simulation to address the challenge of SAR target recognition under ZSL conditions. Additionally, to reduce the domain shift between the simulated domain and the measured domain, we propose a domain adaptation method based on dynamic weight domain loss and classification loss. The effectiveness of semantic information-based 3D models has been validated on the MSTAR dataset and the feasibility of the proposed framework has been validated on the self-built civilian vehicle dataset. The experimental results demonstrate that the first proposed SAR simulation method based on a visual language model and generative diffusion model can effectively improve target recognition performance under ZSL conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. A Multi-Level Cross-Attention Image Registration Method for Visible and Infrared Small Unmanned Aerial Vehicle Targets via Image Style Transfer.
- Author
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Jiang, Wen, Pan, Hanxin, Wang, Yanping, Li, Yang, Lin, Yun, and Bi, Fukun
- Subjects
IMAGE fusion ,INFRARED imaging ,TRACKING algorithms ,VISIBLE spectra ,DEEP learning ,IMAGE registration ,PIXELS - Abstract
Small UAV target detection and tracking based on cross-modality image fusion have gained widespread attention. Due to the limited feature information available from small UAVs in images, where they occupy a minimal number of pixels, the precision required for detection and tracking algorithms is particularly high in complex backgrounds. Image fusion techniques can enrich the detailed information for small UAVs, showing significant advantages under extreme lighting conditions. Image registration is a fundamental step preceding image fusion. It is essential to achieve accurate image alignment before proceeding with image fusion to prevent severe ghosting and artifacts. This paper specifically focused on the alignment of small UAV targets within infrared and visible light imagery. To address this issue, this paper proposed a cross-modality image registration network based on deep learning, which includes a structure preservation and style transformation network (SPSTN) and a multi-level cross-attention residual registration network (MCARN). Firstly, the SPSTN is employed for modality transformation, transferring the cross-modality task into a single-modality task to reduce the information discrepancy between modalities. Then, the MCARN is utilized for single-modality image registration, capable of deeply extracting and fusing features from pseudo infrared and visible images to achieve efficient registration. To validate the effectiveness of the proposed method, comprehensive experimental evaluations were conducted on the Anti-UAV dataset. The extensive evaluation results validate the superiority and universality of the cross-modality image registration framework proposed in this paper, which plays a crucial role in subsequent image fusion tasks for more effective target detection. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. A Two-Component Polarimetric Target Decomposition Algorithm with Grassland Application.
- Author
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Huang, Pingping, Chen, Yalan, Li, Xiujuan, Tan, Weixian, Chen, Yuejuan, Yang, Xiangli, Dong, Yifan, Lv, Xiaoqi, and Li, Baoyu
- Subjects
PLANT anatomy ,GRASSLAND plants ,REMOTE sensing ,GRASSLANDS ,ANALYTICAL solutions - Abstract
The study of the polarimetric target decomposition algorithm with physical scattering models has contributed to the development of the field of remote sensing because of its simple and clear physical meaning with a small computational effort. However, most of the volume scattering models in these algorithms are for forests or crops, and there is a lack of volume scattering models for grasslands. In order to improve the accuracy of the polarimetric target decomposition algorithm adapted to grassland data, in this paper, a novel volume scattering model is derived considering the characteristics of real grassland plant structure and combined with the backward scattering coefficients of grass, which is abstracted as a rotatable ellipsoid of variable shape. In the process of rotation, the possibility of rotation is considered in two dimensions, the tilt angle and canting angle; for particle shape, the anisotropy degree A is directly introduced as a parameter to describe and expand the applicability of the model at the same time. After obtaining the analytical solution of the parameters and using the principle of least negative power to determine the optimal solution of the model, the algorithm is validated by applying it to the C-band AirBorne dataset of Hunshandak grassland in Inner Mongolia and the X-band Cosmos-Skymed dataset of Xiwuqi grassland in Inner Mongolia. The performance of the algorithm with five polarimetric target decomposition algorithms is studied comparatively. The experimental results show that the algorithm proposed in this paper outperforms the other algorithms in terms of grassland decomposition accuracy on different bands of data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
38. Evaluation and Improvement of a CALIPSO-Based Algorithm for Cloud Base Height in China.
- Author
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Li, Ruolin and Ma, Xiaoyan
- Subjects
CLOUD computing ,LIDAR ,ALGORITHMS ,AEROSOLS ,ALTITUDES ,TROPOSPHERIC aerosols - Abstract
Clouds are crucial in regulating the Earth's energy budget. Global cloud top heights have been easily retrieved from satellite measurements, but there are few methods for determining cloud base height (CBH) from satellite measurements. The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm was proposed to derive the height of the lower-troposphere liquid cloud base by using the Cloud-Aerosol Lidar with Orthogonal polarization cloud aerosol LiDAR (CALIOP) profiles and weather observations at airports from aviation routine and special weather report (METARs and SPECIs, called METAR) observation data in the United States. A modification to the CBASE algorithm over China (CNMETAR-CBASE) is presented in this paper. In this paper, the ability of the CBASE algorithm to calculate CBH in China is evaluated, and METAR observations over China (CNMETAR) were then used to modify the CBASE algorithm. The results including CNMETAR observation data in China can better retrieve CBH over China compared with the results using the original CBASE algorithm, and the accuracy of the global CBH results has been improved. Overestimations of CBH with the original algorithm range from 500 to 800 m in China, which have been reduced to about 300 m with an improved algorithm. The deviations calculated by the algorithm also have a significant reduction, from 480 m (CBASE) to 420 m (CNMETAR-CBASE). In conclusion, the modified CBASE algorithm not only calculates the CBH more accurately in China but also improves the results of the global CBH retrieved from satellites. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
39. Ionograms Trace Extraction Method Based on Multiscale Transformer Network.
- Author
-
Han, Sijia, Guo, Wei, and Wang, Caiyun
- Subjects
TRANSFORMER models ,ELECTRON density ,NETWORK performance ,SIGNAL processing ,IONOSPHERE ,DEEP learning - Abstract
The echo traces in the ionograms contain key information about the ionosphere. Therefore, the accurate extraction of these traces is crucial for the subsequent work. This paper transforms the original signal processing problem into a semantic segmentation task, combines it with the currently popular deep learning techniques, and proposes a multiscale Transformer network to achieve pixel-level trace extraction. To train the proposed model, we built a dataset by discretizing the original echo data, labeling, and other preprocessing work. A series of advanced semantic segmentation networks are utilized for comparative experiments. The analysis of the results indicates that the proposed network excels in performance, achieving the highest scores on key semantic segmentation evaluation metrics, including mIoU, Kappa, Dice, and AUC-ROC. In addition, this paper also designs a series of ablation experiments to observe the changes in network performance and to evaluate the rationality of the network design. The experimental results demonstrate the effectiveness of the network in the trace extraction task, which plays a positive role in the subsequent electron density reversal work. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers.
- Author
-
Nguyen, Teo, Liquet, Benoît, Mengersen, Kerrie, and Sous, Damien
- Subjects
- *
CORAL reefs & islands , *MARINE biodiversity , *CORALS , *SPATIAL resolution , *REMOTE-sensing images , *SUPPORT vector machines , *THEMATIC mapper satellite - Abstract
Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
41. Remote Sensing Best Paper Award for the Year 2015.
- Author
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Thenkabail, Prasad S.
- Subjects
REMOTE sensing ,REMOTE sensing periodicals ,AWARDS - Abstract
The article announces the recipients of the Best Paper awards of "Remote Sensing" magazine for 2015 which include those by Hartmut Boesch et al, Curtis Edson et al, and Claudia Kuenzer et al.
- Published
- 2015
- Full Text
- View/download PDF
42. Improved Cycle-Consistency Generative Adversarial Network-Based Clutter Suppression Methods for Ground-Penetrating Radar Pipeline Data.
- Author
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Lin, Yun, Wang, Jiachun, Ma, Deyun, Wang, Yanping, and Ye, Shengbo
- Subjects
GROUND penetrating radar ,GENERATIVE adversarial networks ,DEEP learning - Abstract
Ground-penetrating radar (GPR) is a widely used technology for pipeline detection due to its fast detection speed and high resolution. However, the presence of complex underground media often results in strong ground clutter interference in the collected B-scan echoes, significantly impacting detection performance. To address this issue, this paper proposes an improved clutter suppression network based on a cycle-consistency generative adversarial network (CycleGAN). By employing the concept of style transfer, the network aims to convert clutter images into clutter-free images. This paper introduces multiple residual blocks into the generator and discriminator, respectively, to improve the feature expression ability of the deep learning model. Additionally, the discriminator incorporates the squeeze and excitation (SE) module, a channel attention mechanism, to further enhance the model's ability to extract features from clutter-free images. To evaluate the effectiveness of the proposed network in clutter suppression, both simulation and measurement data are utilized to compare and analyze its performance against traditional clutter suppression methods and deep learning-based methods, respectively. From the result of the measured data, it can be found that the improvement factor ( I m ) of the proposed method has reached 40.68 dB, which is a significant improvement compared to the previous network. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. Radargrammetric 3D Imaging through Composite Registration Method Using Multi-Aspect Synthetic Aperture Radar Imagery.
- Author
-
Luo, Yangao, Deng, Yunkai, Xiang, Wei, Zhang, Heng, Yang, Congrui, and Wang, Longxiang
- Subjects
SYNTHETIC aperture radar ,THREE-dimensional imaging ,SYNTHETIC apertures ,SPECKLE interference ,DIGITAL elevation models ,IMAGE registration ,RADIO telescopes - Abstract
Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges in achieving accurate stereo SAR image matching. To address these aforementioned challenges, this paper proposes a radargrammetric method based on the composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged using the proposed radargrammetric 3D imaging method, resulting in the 3D imaging of target scenes based on multi-aspect SAR images. For validation purposes, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency, while enhancing the imaging accuracy of crucial areas with limited data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review.
- Author
-
Cavalli, Rosa Maria
- Subjects
COASTAL mapping ,GEOGRAPHIC names ,DATA mapping ,LAND cover ,URBAN growth ,COASTS - Abstract
Since 1971, remote sensing techniques have been used to map and monitor phenomena and parameters of the coastal zone. However, updated reviews have only considered one phenomenon, parameter, remote data source, platform, or geographic region. No review has offered an updated overview of coastal phenomena and parameters that can be accurately mapped and monitored with remote data. This systematic review was performed to achieve this purpose. A total of 15,141 papers published from January 2021 to June 2023 were identified. The 1475 most cited papers were screened, and 502 eligible papers were included. The Web of Science and Scopus databases were searched using all possible combinations between two groups of keywords: all geographical names in coastal areas and all remote data and platforms. The systematic review demonstrated that, to date, many coastal phenomena (103) and parameters (39) can be mapped and monitored using remote data (e.g., coastline and land use and land cover changes, climate change, and coastal urban sprawl). Moreover, the authors validated 91% of the retrieved parameters, retrieved from remote data 39 parameters that were mapped or monitored 1158 times (88% of the parameters were combined together with other parameters), monitored 75% of the parameters over time, and retrieved 69% of the parameters from several remote data and compared the results with each other and with available products. They obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. They combined 17% of the parameters that were retrieved with GIS and model techniques. In conclusion, the authors addressed the requirements needed to more effectively analyze coastal phenomena and parameters employing integrated approaches: they retrieved the parameters from different remote data, merged different data and parameters, compared different methods, and combined different techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. On-Board Multi-Class Geospatial Object Detection Based on Convolutional Neural Network for High Resolution Remote Sensing Images.
- Author
-
Shen, Yanyun, Liu, Di, Chen, Junyi, Wang, Zhipan, Wang, Zhe, and Zhang, Qingling
- Subjects
OBJECT recognition (Computer vision) ,CONVOLUTIONAL neural networks ,REMOTE-sensing images ,REMOTE sensing ,DATA transmission systems ,URBAN planning ,OPTICAL remote sensing - Abstract
Multi-class geospatial object detection in high-resolution remote sensing images has significant potential in various domains such as industrial production, military warning, disaster monitoring, and urban planning. However, the traditional process of remote sensing object detection involves several time-consuming steps, including image acquisition, image download, ground processing, and object detection. These steps may not be suitable for tasks with shorter timeliness requirements, such as military warning and disaster monitoring. Additionally, the transmission of massive data from satellites to the ground is limited by bandwidth, resulting in time delays and redundant information, such as cloud coverage images. To address these challenges and achieve efficient utilization of information, this paper proposes a comprehensive on-board multi-class geospatial object detection scheme. The proposed scheme consists of several steps. Firstly, the satellite imagery is sliced, and the PID-Net (Proportional-Integral-Derivative Network) method is employed to detect and filter out cloud-covered tiles. Subsequently, our Manhattan Intersection over Union (MIOU) loss-based YOLO (You Only Look Once) v7-Tiny method is used to detect remote-sensing objects in the remaining tiles. Finally, the detection results are mapped back to the original image, and the truncated NMS (Non-Maximum Suppression) method is utilized to filter out repeated and noisy boxes. To validate the reliability of the scheme, this paper creates a new dataset called DOTA-CD (Dataset for Object Detection in Aerial Images-Cloud Detection). Experiments were conducted on both ground and on-board equipment using the AIR-CD dataset, DOTA dataset, and DOTA-CD dataset. The results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
46. Precise Motion Compensation of Multi-Rotor UAV-Borne SAR Based on Improved PTA.
- Author
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Cheng, Yao, Qiu, Xiaolan, and Meng, Dadi
- Subjects
IMAGE stabilization ,SYNTHETIC aperture radar ,GROUND motion ,NUMERICAL calculations ,ELECTRONIC data processing - Abstract
In recent years, with the miniaturization of high-precision position and orientation systems (POS), precise motion errors during SAR data collection can be calculated based on high-precision POS. However, compensating for these errors remains a significant challenge for multi-rotor UAV-borne SAR systems. Compared with large aircrafts, multi-rotor UAVs are lighter, slower, have more complex flight trajectories, and have larger squint angles, which result in significant differences in motion errors between building targets and ground targets. If the motion compensation is based on ground elevation, the motion error of the ground target will be fully compensated, but the building target will still have a large residual error; as a result, although the ground targets can be well-focused, the building targets may be severely defocused. Therefore, it is necessary to further compensate for the residual motion error of building targets based on the actual elevation on the SAR image. However, uncompensated errors will affect the time–frequency relationship; furthermore, the ω-k algorithm will further change these errors, resulting in errors in SAR images becoming even more complex and difficult to compensate for. To solve this problem, this paper proposes a novel improved precise topography and aperture-dependent (PTA) method that can precisely compensate for motion errors in the UAV-borne SAR system. After motion compensation and imaging processing based on ground elevation, a secondary focus is applied to defocused buildings. The improved PTA fully considers the coupling of the residual error with the time–frequency relationship and ω-k algorithm, and the precise errors in the two-dimensional frequency domain are determined through numerical calculations without any approximations. Simulation and actual data processing verify the effectiveness of the method, and the experimental results show that the proposed method in this paper is better than the traditional method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. An Improved Remote Sensing Retrieval Method for Elevated Duct in the South China Sea.
- Author
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Cheng, Yinhe, Zha, Mengling, Qiao, Wenli, He, Hongjian, Wang, Shuwen, Wang, Shengxiang, Li, Xiaoran, and He, Weiye
- Subjects
MODIS (Spectroradiometer) ,STANDARD deviations ,REMOTE sensing ,ELECTROMAGNETIC waves ,REFRACTIVE index - Abstract
Elevated duct is an atmospheric structure characterized by abnormal refractive index gradients, which can significantly affect the performance of radar, communication, and other systems by capturing a portion of electromagnetic waves. The South China Sea (SCS) is a high-incidence area for elevated duct, so conducting detection and forecasts of the elevated duct in the SCS holds important scientific significance and practical value. This paper attempts to utilize remote sensing techniques for extracting elevated duct information. Based on GPS sounding data, a lapse rate formula (LRF) model and an empirical formula (EF) model for the estimation of the cloud top height of Stratocumulus were obtained, and then remote sensing retrieval methods of elevated duct were established based on the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. The results of these two models were compared with results from the elevated duct remote sensing retrieval model developed by the United States Naval Postgraduate School. It is shown that the probability of elevated duct events was 79.1% when the presence of Stratocumulus identified using GPS sounding data, and the trapping layer bottom height of elevated duct well with the cloud top height of Stratocumulus, with a correlation coefficient of 0.79, a mean absolute error of 289 m, and a root mean square error of 598 m. Among the different retrieval models applied to MODIS satellite data, the LRF model emerged as the optimal remote sensing retrieval method for elevated duct in the SCS, showing a correlation coefficient of 0.51, a mean absolute error of 447 m, and a root mean square error of 658 m between the trapping layer bottom height and the cloud top height. Consequently, the encouraging validation results demonstrate that the LRF model proposed in this paper offers a novel method for diagnosing and calculating elevated ducts information over large-scale marine areas from remote sensing data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. Evolution of the Floe Size Distribution in Arctic Summer Based on High-Resolution Satellite Imagery.
- Author
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Li, Zongxing, Lu, Peng, Zhou, Jiaru, Zhang, Hang, Huo, Puzhen, Yu, Miao, Wang, Qingkai, and Li, Zhijun
- Subjects
DISTRIBUTION (Probability theory) ,WEIBULL distribution ,IMAGE processing ,REMOTE-sensing images ,FRACTAL dimensions - Abstract
In this paper, based on high-resolution satellite images near an ice bridge in the Canadian Basin, we extracted floe size parameters and analyzed the temporal and spatial variations in the parameters through image processing techniques. The floe area shows a decreasing trend over time, while the perimeter and mean clamped diameter (MCD) exhibit no obvious pattern of change. In addition, the roundness of floes, reflected by shape parameters, generally decreases initially and then increases, and the average roundness of small floes is smaller than that of large floes. To correct the deviations from power law behaviour when assessing the floe size distribution (FSD) with the traditional power law function, the upper-truncated power law distribution function and the Weibull function are selected. The four parameters of the two functions are important parameters for describing the floe size distribution, and L r and L 0 are roughly equal to the maximum calliper diameter and the average calliper diameter of the floes in the region. D in the upper-truncated power law distribution function represents the fractal dimension of the floes, and r in the Weibull function represents the shape parameter of the floes, both of which increase and then decrease with time. In this paper, we investigate the response of the rate of change in the FSD parameter to the differences in the monthly average temperature and find that D , r and air temperature are positively correlated, which verifies the influence of air temperature on the floe size distribution. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Enhanced Strapdown Inertial Navigation System (SINS)/LiDAR Tightly Integrated Simultaneous Localization and Mapping (SLAM) for Urban Structural Feature Weaken Occasions in Vehicular Platform.
- Author
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Xu, Xu, Guan, Lianwu, Gao, Yanbin, Chen, Yufei, and Liu, Zhejun
- Subjects
INERTIAL navigation systems ,KALMAN filtering ,POINT cloud ,LIDAR ,TRACKING algorithms - Abstract
LiDAR-based simultaneous localization and mapping (SLAM) offer robustness against illumination changes, but the inherent sparsity of LiDAR point clouds poses challenges for continuous tracking and navigation, especially in feature-deprived scenarios. This paper proposes a novel LiDAR/SINS tightly integrated SLAM algorithm designed to address the localization challenges in urban environments characterized in sparse structural features. Firstly, the method extracts edge points from the LiDAR point cloud using a traditional segmentation method and clusters them to form distinctive edge lines. Then, a rotation-invariant feature—line distance—is calculated based on the edge line properties that were inspired by the traditional tightly integrated navigation system. This line distance is utilized as the observation in a Kalman filter that is integrated into a tightly coupled LiDAR/SINS system. This system tracks the same edge lines across multiple frames for filtering and correction instead of tracking points or LiDAR odometry results. Meanwhile, for loop closure, the method modifies the common SCANCONTEXT algorithm by designating all bins that do not reach the maximum height as special loop keys, which reduce false matches. Finally, the experimental validation conducted in urban environments with sparse structural features demonstrated a 17% improvement in positioning accuracy when compared to the conventional point-based methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Underwater Side-Scan Sonar Target Detection: YOLOv7 Model Combined with Attention Mechanism and Scaling Factor.
- Author
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Wen, Xin, Wang, Jian, Cheng, Chensheng, Zhang, Feihu, and Pan, Guang
- Subjects
SONAR ,ARTIFICIAL neural networks ,SONAR imaging ,OBJECT recognition (Computer vision) ,UNDERWATER exploration - Abstract
Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence of a few highlighted areas on the targets, blurred feature details, and difficulty in collecting data from side-scan sonar, achieving high-precision autonomous target recognition in side-scan sonar images is challenging. This article addresses this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in side-scan sonar images. Firstly, given that side-scan sonar images contain large areas of irrelevant information, this paper introduces the Swin-Transformer for dynamic attention and global modeling, which enhances the model's focus on the target regions. Secondly, the Convolutional Block Attention Module (CBAM) is utilized to further improve feature representation and enhance the neural network model's accuracy. Lastly, to address the uncertainty of geometric features in side-scan sonar target features, this paper innovatively incorporates a feature scaling factor into the YOLOv7 model. The experiment initially verified the necessity of attention mechanisms in the public dataset. Subsequently, experiments on our side-scan sonar (SSS) image dataset show that the improved YOLOv7 model has 87.9% and 49.23% in its average accuracy ( m A P 0.5 ) and ( m A P 0.5:0.95), respectively. These results are 9.28% and 8.41% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this paper has great potential for object detection and the recognition of side-scan sonar images. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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