104 results on '"Liu, Shanwei"'
Search Results
2. A multi-module with a two-way feedback method for Ulva drift-diffusion
- Author
-
Sheng, Hui, Li, Jianmeng, Wang, Qimao, Zou, Bin, Shi, Lijian, Xu, Mingming, Liu, Shanwei, Wan, Jianhua, Zeng, Zhe, and Chen, Yanlong
- Published
- 2023
- Full Text
- View/download PDF
3. YOLOv8-BYTE: Ship tracking algorithm using short-time sequence SAR images for disaster response leveraging GeoAI
- Author
-
Muhammad Yasir, Liu Shanwei, Xu Mingming, Wan Jianhua, Sheng Hui, Shah Nazir, Xin Zhang, and Arife Tugsan Isiacik Colak
- Subjects
Multi-Object Tracking (MOT) ,YOLOv8-BYTE ,Tracking Ship ,Kalman Filters (KF) ,Synthetic Aperture Radar (SAR) ,Disaster Response Leveraging GeoAI ,Physical geography ,GB3-5030 ,Environmental sciences ,GE1-350 - Abstract
Ship tracking technology is crucial for emergency rescue in the event of a disaster. Quickly identifying the position and status of vessels is vital for rescue teams to be able to deploy efficiently in disaster areas. When responding to emergencies or natural disasters, ship tracking technology plays a critical role in supporting emergency rescue operations and resource allocation, improving the overall resilience of the maritime transportation system. However, the research on multi-object tracking (MOT) algorithms has primarily focused on optical image datasets. In contrast, image data from synthetic aperture radar (SAR) presents unique challenges, such as defocus interference, a high false alarm rate, and a lack of prior samples. To overcome these particular challenges, we propose a robust MOT algorithm developed for SAR images to achieve effective multi-vessel tracking under difficult imaging conditions. In particular, we optimize the YOLOv8 detection network by introducing a diffusion model-based training method for data augmentation. This method improves the robustness of the network to scaling, rotational and translational deformations. Moreover, an enhanced swin transformer is proposed as a feature extraction network, which strengthens the representation capability of the detection network. Furthermore, the state parameters within the KF technique are enhanced by directly capturing the details of the height and width of the tracking rectangle box. This refinement of the ByteTrack algorithm aims to achieve a more precise and accurate fit of the tracking rectangle to the ship, further improving the overall tracking performance. The experimental results from the ship detection and multiple objects tracking datasets show the impressive performance of the proposed model. With a precision of 97.60%, a recall of 96.36%, and an average precision of 96.72%, the model achieves exceptional detection accuracy with an 18% reduction in model parameters. Furthermore, significant improvements can be observed in key tracking metrics such as HOTA, MOTA and IDF1, with improvements of 4.8%, 8.5% and 6.8% respectively compared to the baseline algorithm, alongside a remarkable 37.5% reduction in IDS. It is noteworthy that the tracker works in real time, achieving an average analysis speed of 47 frames per second. The proposed MOT algorithm achieves state-of-art tracking performance on a SAR image dataset with short time sequences. Therefore, the proposed approach is a compelling solution for ship tracking in SAR imagery.
- Published
- 2024
- Full Text
- View/download PDF
4. Investigation of the causes and mechanisms of hypoxia in the central Bohai Sea in the summer of 2022
- Author
-
Guo, Jie, Jin, Yong, Liu, Shanwei, Li, Tao, Ji, Diansheng, Hou, Chawei, and Tang, Haitian
- Published
- 2024
- Full Text
- View/download PDF
5. A new decomposition model of sea level variability for the sea level anomaly time series prediction
- Author
-
Sun, Qinting, Wan, Jianhua, Liu, Shanwei, Jiang, Jinghui, and Muhammad, Yasir
- Published
- 2023
- Full Text
- View/download PDF
6. Temperature scaling unmixing framework based on convolutional autoencoder
- Author
-
Xu, Jin, Xu, Mingming, Liu, Shanwei, Sheng, Hui, and Yang, Zhiru
- Published
- 2024
- Full Text
- View/download PDF
7. Ulva Prolifera subpixel mapping with multiple-feature decision fusion
- Author
-
Wan, Jianhua, Wan, Xianci, Sun, Lie, Xu, Mingming, Sheng, Hui, Liu, Shanwei, Zou, Bin, and Wang, Qimao
- Published
- 2023
- Full Text
- View/download PDF
8. Maritime risk assessment using a non-linear spatial multi-criteria decision method: A case study in the Bohai Sea and Yellow Sea, China
- Author
-
Du, Pei, Zeng, Zhe, Shen, Yongtian, and Liu, Shanwei
- Published
- 2023
- Full Text
- View/download PDF
9. Morphological change assessment of a coastal island in SE Bangladesh reveal high accumulation rates
- Author
-
Hossain, Md Sakaouth, Yasir, Muhammad, Shahriar, Md. Shams, Jahan, Maftuha, Liu, Shanwei, and Niang, Abdoul Jelil
- Published
- 2023
- Full Text
- View/download PDF
10. SLWE-Net: An improved lightweight U-Net for Sargassum extraction from GOCI images
- Author
-
Song, Lei, Chen, Yanlong, Liu, Shanwei, Xu, Mingming, and Cui, Jianyong
- Published
- 2023
- Full Text
- View/download PDF
11. An improved semantic segmentation model based on SVM for marine oil spill detection using SAR image
- Author
-
Wang, Dawei, Liu, Shanwei, Zhang, Chao, Xu, Mingming, Yang, Junfang, Yasir, Muhammad, and Wan, Jianhua
- Published
- 2023
- Full Text
- View/download PDF
12. An adversarial learning approach to forecasted wind field correction with an application to oil spill drift prediction
- Author
-
Li, Yongqing, Huang, Weimin, Lyu, Xinrong, Liu, Shanwei, Zhao, Zhe, and Ren, Peng
- Published
- 2022
- Full Text
- View/download PDF
13. Multi-scale ship target detection using SAR images based on improved Yolov5
- Author
-
Muhammad Yasir, Liu Shanwei, Xu Mingming, Sheng Hui, Md Sakaouth Hossain, Arife Tugsan Isiacik Colak, Dawei Wang, Wan Jianhua, and Kinh Bac Dang
- Subjects
synthetic aperture radar (SAR) ,ship identification ,artificial intelligence ,deep learning (DL) ,YOLOv5S ,SAR ship detection dataset (SSDD) ,Science ,General. Including nature conservation, geographical distribution ,QH1-199.5 - Abstract
Synthetic aperture radar (SAR) imaging is used to identify ships, which is a vital task in the maritime industry for managing maritime fisheries, marine transit, and rescue operations. However, some problems, like complex background interferences, various size ship feature variations, and indistinct tiny ship characteristics, continue to be challenges that tend to defy accuracy improvements in SAR ship detection. This research study for multiscale SAR ships detection has developed an upgraded YOLOv5s technique to address these issues. Using the C3 and FPN + PAN structures and attention mechanism, the generic YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. The SAR ship detection datasets and AirSARship datasets, along with two SAR large scene images acquired from the Chinese GF-3 satellite, are utilized to determine the experimental results. This model’s applicability is assessed using a variety of validation metrics, including accuracy, different training and test sets, and TF values, as well as comparisons with other cutting-edge classification models (ARPN, DAPN, Quad-FPN, HR-SDNet, Grid R-CNN, Cascade R-CNN, Multi-Stage YOLOv4-LITE, EfficientDet, Free-Anchor, Lite-Yolov5). The performance values demonstrate that the suggested model performed superior to the benchmark model used in this study, with higher identification rates. Additionally, these excellent identification rates demonstrate the recommended model’s applicability for maritime surveillance.
- Published
- 2023
- Full Text
- View/download PDF
14. A comprehensive evaluation of utilizing BeiDou data to estimate snow depths from two ground-based stations
- Author
-
Liu, Shanwei, Zhang, Jie, Wan, Wei, Liang, Hong, Liu, Baojian, and Guo, Zhizhou
- Published
- 2022
- Full Text
- View/download PDF
15. The Impact of Pedestrian Lane Formation by Obstacles on Fire Evacuation Efficiency in the Presence of Unfair Competition.
- Author
-
Liu, Shanwei, Li, Xiao, Peng, Bozhezi, and Li, Chaoyang
- Subjects
- *
UNFAIR competition , *CIVILIAN evacuation , *SOCIAL forces , *PEDESTRIANS , *CROWDS , *SCARCITY - Abstract
After a fire breaks out, pedestrians simultaneously move towards the exit and quickly form a crowded area near the exit. With the intensification of pedestrians' tendencies towards unfair competition, there is an increase in pushing and collisions within the crowd. The possibility of stampedes within the crowd also gradually increases. Analyzing the causes and psychological tendencies behind pedestrian pushing and collisions has a positive effect on reducing crowd instability and improving evacuation efficiency. This research proposes a modified social force model considering the unfair competition tendency of pedestrians. The model considers factors such as the gap between pedestrians' actual and maximum achievable speed, effective radius, and their distance from the exit. In order to overcome the shortage of "deadlock" in the classical social force model in a high-density environment, this research introduces the feature of variable pedestrian effective radius. The effective radius of pedestrians dynamically changes according to the density of the surrounding crowd and queuing time. Through validation, the evacuation efficiency of this model aligns well with the actual situation and effectively reflects pedestrians' pushing and squeezing behaviors in high-density environments. This research also analyzes how to strategically arrange obstacles to mitigate the exacerbating effect of unfair pedestrian competition on exit congestion. Five experiments were conducted to analyze how the relative position of obstacles and exits, the number of evacuation paths, and the size of the obstacle-free area before the exit affect evacuation efficiency in the presence of unfair pedestrian competition. The results show that evacuation efficiency can be improved when obstacles play a role in guiding or reducing the interaction of pedestrians in different queues. However, when obstacles hinder pedestrians, the evacuation efficiency is reduced to a certain extent. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
16. A multi-scale context-aware and batch-independent lightweight network for green tide extraction from SAR images.
- Author
-
Xu, Mingming, Zhu, Xiaofang, Liu, Yanfen, Liu, Shanwei, and Sheng, Hui
- Subjects
SYNTHETIC aperture radar ,SPECKLE interference ,DEEP learning ,HUMAN ecology - Abstract
The outbreaks of green tide have caused severe harm to the marine environment and human society. Synthetic Aperture Radar (SAR) plays an important role in green tide monitoring by virtue of its high resolution and cloud-free nature. The existing green tide extraction methods still face challenges in identifying multi-scale green tide patches due to noise interference, uneven greyscale and blurred boundaries in SAR images. Meanwhile, the practical application of deep learning methods with high precision is limited due to the complexity of the model and the large amount of computation. Therefore, we propose a multi-scale context-aware and batch-independent lightweight green tide extraction network called MBL-Net. A novel lightweight heterogeneous backbone is designed to extract multi-scale discriminative features and improve segmentation efficiency by using multi-scale selection kernel (MSK) modules and lightweight stages. Meanwhile, Triplet attention module is introduced to improve the internal consistency of the green tide region and suppress the effect of speckle noise. Then, the mixed pooling-based channel prior module (MCPM) is used to expand the receptive field of the network and extract the fine green tide structure by fusing multi-scale features. In addition, Filter Response Normalisation (FRN) is innovatively applied for feature normalization in the decoding stage, eliminating batch dependency. In order to verify the effectiveness of the proposed method, a dataset is built using the Sentinel-1 images of the Yellow Sea, China, from 2019 to 2021. The experimental results show that the proposed method achieves an overall accuracy of 98.59% with 0.970 G FLOPs and 3.525 M parameters, which ensures high precision and improves green tide detection efficiency. Compared with several representative networks, this method can capture more details of green tide with fewer parameters and faster calculation speed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
17. Coastline extraction based on multi-scale segmentation and multi-level inheritance classification
- Author
-
Sheng Hui, Guo Mengliang, Gan Yuliang, Xu Mingming, Liu Shanwei, Muhammad Yasir, Cui Jianyong, and Wan Jianhua
- Subjects
GF-2 images ,multi-scale segmentation ,multi-level inheritance classification ,automatic coastline extraction ,coastline types ,Science ,General. Including nature conservation, geographical distribution ,QH1-199.5 - Abstract
Detailed management of the coastline is critical to the development of coastal states. However, the current classification of the coastline is relatively weak. This study proposed an automatic method to detect coastlines with category attributes based on multi-scale segmentation and multi-level inheritance classification. Fully integrating the advantages of multi-scale segmentation and multi-level classification, it solved the problems that traditional methods could not solve, such as extracting coastlines with categorical attributes, cultivation ponds that are easily affected by tidal flats, and complex coastal terrain. The Chinese GF-2 satellite images are used to extract various types of coastlines in Jiaozhou Bay and its surrounding areas such as the harbor-wharf coastline, silt coastline, pond coastline, rocky coastline, and sandy coastline. Compared with the human interpretation, it is found that the coastline extracted by our proposed method is different by 10.104 km in the harbor-wharf coastline, 0.099 km in the silt coastline, 2.677 km in the pond coastline, 8.831 km in the rocky coastline, and 0.218 km in the sandy coastline. Furthermore, compared to the object-based region growing integrating edge detection (OBRGIE) method, it is increased by 13.52%, 2.16%, 14.48%, 52.57%, and 22.97%, respectively. The results show that our proposed method is algorithmically more reasonable, accurate, and powerful. It can provide data support for refined coastline management.
- Published
- 2022
- Full Text
- View/download PDF
18. A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images
- Author
-
Wang, Wandi, Sheng, Hui, Chen, Yanlong, Liu, Shanwei, Mao, Jijun, Zeng, Zhe, and Wan, Jianhua
- Published
- 2021
- Full Text
- View/download PDF
19. Performance Improvement of Single Screw Compressor by Meshing Clearance Adjustment Used in Refrigeration System
- Author
-
Lu, Yuanwei, Liu, Shanwei, Wu, Yuting, Lei, Biao, Zhi, Ruiping, Wen, Qiangyu, and Ma, Chongfang
- Published
- 2021
- Full Text
- View/download PDF
20. Theoretical and Experimental Research on Thermal Dynamic Characteristics of Single-Screw Compressor with a New Composite SLIDE Valve.
- Author
-
Liu, Shanwei, Zhi, Ruiping, Wu, Yuting, Lu, Yuanwei, Lei, Biao, and Ma, Chongfang
- Subjects
- *
VALVES , *COMPRESSORS , *HEAT capacity , *PARTIAL discharges - Abstract
Based on the single-screw compressor (SSC) structure, a new type of composite slide valve (CSV) has been proposed and designed, featuring internal volume ratios of 2.8, 3.9, and 5.6 and operating under a partial load of 35%. The theoretical model describing the dynamic features and thermodynamic performance of the SSC with CSV has also been built. The pressure ratio of the experimental system can be adjusted from 3.3 to 7.8, and the experimental results demonstrate the CSV's effective performance. The deviations between the calculated and measured results for volume ratio and input power are 3.33–9.08% and 0.32–8.03%, and the deviations for heating capacity and adiabatic efficiency range from 0.92–8.73% to 2.09–9.67%, respectively. The introduction of the CSV offers a novel approach to enhancing SSC efficiency. Both the theoretical and experimental findings lay a foundation for future optimization and design improvements in variable load and internal volumetric ratio single-screw compressors. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
21. Hyperspectral anomaly detection based on adaptive background dictionary construction and collaborative representation.
- Author
-
Xu, Mingming, Zhang, Jinhao, Liu, Shanwei, and Sheng, Hui
- Subjects
INTRUSION detection systems (Computer security) ,ENCYCLOPEDIAS & dictionaries ,PIXELS ,TRAFFIC monitoring - Abstract
Collaborative representation-based (CR) methods have received widespread attention in hyperspectral anomaly detection, but the results are greatly affected by the quality of the background dictionary. Abnormal pixels and abnormal-mixed pixels in the background dictionary may affect the accuracy of linear representation and make its performance poor. To address the above problem, an adaptive background dictionary construction-based anomaly detection method is proposed. To ensure the purity of the background dictionary, abnormal pixels and abnormal-mixed pixels around each test pixel are removed adaptively through clustering and pure pixel extraction. Furthermore, the saliency weight of the test pixel is calculated through the pixels in the inner window and weighted into the linear representation process to improve the robustness of the method. Experimental results on four hyperspectral datasets show that the proposed method performs better than other CR methods and traditional detectors, and it can reduce the dependence of anomaly detection performance on dual window size. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. Performance optimization of a heat pump integrated with a single-screw refrigeration compressor with liquid refrigerant injection
- Author
-
Wen, Qiangyu, Zhi, Ruiping, Wu, Yuting, Lei, Biao, Liu, Shanwei, and Shen, Lili
- Published
- 2020
- Full Text
- View/download PDF
23. Remote sensing monitoring of seagrass bed dynamics using cross-temporal-spatial domain transfer learning in Yellow river Delta.
- Author
-
Meng, Ziyue, Hu, Yabin, Ren, Guangbo, Zhu, Wenqing, Wang, Jianbu, Liu, Shanwei, and Ma, Yi
- Subjects
SEAGRASSES ,REMOTE sensing ,IMAGE recognition (Computer vision) ,RIVER channels ,LANDSAT satellites ,REMOTE-sensing images - Abstract
Seagrass beds play a vital ecological role by regulating climate, maintaining biodiversity, and sequestering carbon. Achieving accurate classification of seagrass beds across both time and space is of significant importance for efficient monitoring. This paper addresses the challenges of seagrass bed classification in remote sensing imagery and the difficulty of achieving temporal and spatial transferability of classification model. Leveraging a long-term Landsat satellite image time series spanning from 1976 to 2022 in the Yellow River Delta, we enhance the DeepLab V3+ network and propose a cross-temporal-spatial domain seagrass bed transfer learning classification approach. We utilize selectively chosen images from a few years in partial regions for training, introduce freeze-training and-unfreeze-training during the model training phase, and then transfer the model to achieve cross-temporal and cross-temporal-spatial domain classification on images from other years and regions. Additionally, we discuss the factors influencing the classification results. The research results indicate that: (1)The method we proposed has shown good results, achieving classification accuracy of 83.17% and 80.99% for seagrass beds in both cross-temporal and cross-temporal-spatial domains. (2) The classification results indicate that this method achieves an average classification accuracy higher by 16.18% compared to networks such as UNet, PSPNet, HRNet, UNet++, SegFormer, etc. under cross-temporal conditions and higher by 17.56% under cross-spatiotemporal conditions for seagrass bed classification. (3) Based on the classification results, it is observed that from 1973 to 1986, the overall area of seagrass beds in the Yellow River Delta had an initial growth trend, with the largest seagrass bed area recorded in 1986, reaching 4281.289 hectares. Subsequently, the seagrass beds showed an overall decreasing trend, and by 2019 they had almost completely disappeared. The cross-temporal-spatial domain seagrass bed transfer learning classification method proposed in this paper can provide valuable support for the monitoring, restoration, management, and utilization of seagrass ecosystems. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
24. MMF-CNN: a multimodal fusion CNN network for winter wheat extraction incorporating active and passive time series data.
- Author
-
Chen, Jingyi, Han, Haifeng, Xu, Mingming, Wan, Jianhua, and Liu, Shanwei
- Subjects
WINTER wheat ,CONVOLUTIONAL neural networks ,LIFE cycles (Biology) ,SUPPORT vector machines ,FEATURE extraction ,REMOTE sensing - Abstract
Timely and accurate acquisition of winter wheat planting areas is crucial for food security. In this study, Sentinel-1 and Sentinel-2 time-series data are integrated at the feature level to enhance the accuracy of winter wheat extraction. However, existing feature-level fusion models suffer from insufficient feature extraction and lack of feature completeness, thereby overlooking the complementarity and correlation between these two modalities. A Multimodal Fusion Convolutional Neural Network (MMF-CNN) model is proposed to address the issues above. Firstly, the images of Sentinel-1 and Sentinel-2 are processed to obtain the NDVI and backscatter characteristics of the winter wheat time series life cycle. A single feature and a combination of two features are then imported into each end of the model. The model adds a feature fusion module, which can fully extract the feature information. At the same time, the original features are retained in the process of multiscale feature fusion, which avoids the loss of the original information. Finally, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Visual Geometry Group (VGG) are selected as the comparison models for comparative experiments, and the classification results of remote sensing images are obtained. These results demonstrate that the joint utilization of SAR and optical data yields the highest classification accuracy, with an F1 score of 97.42% for winter wheat. The overall accuracy (OA) of the proposed MMF-CNN method in this study is 96.87%, representing a 1.86% improvement compared to the Conv1D-CNN model. This improvement signifies adaptive feature learning at different hierarchical levels. Comparing the accuracy with other mainstream methods, the OA improves by 1.75%–4.37%, reveals finer ground details, and demonstrates faster performance. This study can provide methodological references for crop extraction studies based on multi-source data and time series analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. Dual-input ultralight multi-head self-attention learning network for hyperspectral image classification.
- Author
-
Li, Xinhao, Xu, Mingming, Liu, Shanwei, Sheng, Hui, and Wan, Jianhua
- Subjects
IMAGE recognition (Computer vision) ,DEEP learning ,PROCESS capability ,ELECTRONIC data processing ,DATA compression ,DATA mining - Abstract
In the hyperspectral image (HSI) classification tasks, various deep learning models have achieved remarkable success. However, most deep learning models are compute-intensive, requiring significant computing power, time, and other resources. It becomes a challenge to pursue better results while saving computational resources. Therefore, a novel dual-input ultralight multi-head self-attention learning network (DUMS-LN) is proposed for HSI classification. The proposed DUMS-LN consists of three main core modules, namely the high-dimensional reduced module (HDRM), lightweight multi-head self-attention (LMHSA) module, and linearized hierarchical conversion module (LHCM). HDRM is used as a pre-processing module with efficient data compression and combines spatial and spectral information extraction from the raw data to provide cleaner and more comprehensive feature data for subsequent processing. In addition, the core computational module of DUMS-LN is the LMHSA module, which is lightweight but possesses better data processing capability than the traditional multi-head self-attention module. Finally, the LHCM divides the model into two phases, reducing the dimensionality of the feature data phase by phase so that the LMHSA module can perform feature extraction at different levels. Experiments on four benchmark HSI datasets show that the proposed DUMS-LN outperforms the comparison HSI classification algorithms regarding speed and classification accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Marine oil spill detection using improved polarimetric feature based on polarization SAR image.
- Author
-
Wang, Dawei, Song, Shasha, Yang, Junfang, Xu, Mingming, Song, Dongmei, Guo, Jie, Wan, Jianhua, and Liu, Shanwei
- Subjects
MICROWAVE remote sensing ,MARINE pollution monitoring ,MARINE pollution ,SYNTHETIC aperture radar ,OIL spills ,ENERGY futures ,RAINFALL - Abstract
Monitoring marine oil pollution holds both practical and scientific significance. Synthetic Aperture Radar (SAR) is an active microwave remote sensing technique capable of all-weather and all-day with fine spatial resolution. However, under low wind conditions, rain cells and young ice are examples of look-alikes affect the accuracy of oil spill detection. Polarimetric SAR assumes a crucial role in this context, as it can extract abundant polarimetric features by polarimetric target decomposition. Drawing inspiration from this advancement, an improved polarimetric feature named relative feature based on Cloude-Pottier target decomposition was proposed. The Jeffries-Matusita distance indicates the substantial potential of the relative feature in detecting oil spills. The improved polarimetric feature within U-Net, FCN-8s, and DeepLabv3+ResNet-18 for oil spill detection using polarization SAR images. Experiment results demonstrated that the relative feature has superior performance compared to other polarimetric features and obtained the highest accuracy and dice within U-Net compared to the other two models. These findings introduce promising concepts for achieving rapid and precise detection of oil spills in future applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. Sea-Level Change Over the China Sea and Its Vicinity Derived from 25-Year T/P Series Altimeter Data
- Author
-
Wan, Jianhua, Sun, Qinting, Liu, Shanwei, and Li, Yinlong
- Published
- 2018
- Full Text
- View/download PDF
28. Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation.
- Author
-
Qin, Dan, Jia, Yongjun, Lin, Mingsen, and Liu, Shanwei
- Subjects
STANDARD deviations ,ALTIMETERS ,REMOTE sensing - Abstract
China's first dynamic environment satellite constellation includes the HY-2B, HY-2C, and HY-2D satellites. In this study, the along track SLA, SWH, and SSWS of this satellite constellation were evaluated. SLA parameters are evaluated using self-crossing and dual-crossing methods. The SSWS and SWH data were evaluated by comparing with NDBC buoy and other available satellites' data. The evaluation revealed that the standard deviation of the SLA from the HY-2B/C/D satellites' single mission crossovers was 3.29 cm, 3.51 cm, and 3.72 cm, respectively. In addition, at the dual-crossovers of the Jason-3 satellite and the HY-2B satellite, the HY-2B satellite, and the HY-2C/D satellites, the standard deviation was determined to be 3.40 cm, 3.48 cm, and 4.25 cm, respectively. The accuracy of the SWH products of the HY-2B/C/D satellite radar altimeters was observed to be 0.23 m, 0.25 m, and 0.26 m, respectively. The accuracy of the SSWS data of the HY-2B/C/D satellite radar altimeters was observed to be 1.48 m/s, 1.59 m/s, and 1.35 m/s, respectively. In addition, this study also analyzed and compared the observation efficiency of the dynamic environment satellite constellation with the following six satellites: Sentinel-3(A, B), Jason-3, Sentinel-6A, Saral, and Cryosat-2. Observation efficiency refers to selection of any point on the globe to find a minimum radius of at least one observation point within a circle in a 14-day period. The analysis results demonstrated that observation efficiency of China's first dynamic environment satellite constellation was comparable to that of the six satellites. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
29. An Improved Lightweight U-Net for Sea Ice Lead Extraction From Multipolarization SAR Images.
- Author
-
Liu, Shanwei, Li, Mocun, Xu, Mingming, and Zeng, Zhe
- Abstract
Precise and fast extraction of sea ice leads is the foundation for polar research and ship navigation. The accuracy of traditional methods for sea ice lead extraction is limited, and the efficiency of deep learning methods is difficult to guarantee. Besides, the tedious preprocessing steps complicate the application of existing methods. In this article, we proposed a lightweight semantic segmentation model based on the U-Net framework for sea ice lead extraction, which introduced lightweight blocks and a feature branch. Lightweight blocks took the place of the convolutional layers in U-Net to reduce the network parameters and increase operational speed. With the input of the contrast feature of horizontal-vertical (HV) polarization, the feature branch was used to improve the extraction precision and robustness. Besides, the combination of the lightweight blocks, feature branch and U-Net framework was beneficial to resist preprocessing. In the experiments, the performance of the proposed method on non-preprocessed Sentinel-1 dataset was better than that of the classical semantic segmentation method on preprocessed Sentinel-1 dataset in floating-point operations (FLOPs), parameters, frames per second (FPS), and accuracy evaluation. The results indicate that the proposed network is effective and lightweight for sea ice lead extraction from non-preprocessed data. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
30. Spatial-Spectral Attention Bilateral Network for Hyperspectral Unmixing.
- Author
-
Yang, Zhiru, Xu, Mingming, Liu, Shanwei, Sheng, Hui, and Zheng, Hongxia
- Abstract
Autoencoders (AEs) are widely utilized in hyperspectral unmixing (HU) as an unsupervised learning model. In particular, convolutional AE networks are popular for processing multidimensional hyperspectral features. Nonetheless, the traditional convolutional AE network’s receptive field is constrained in the unmixing task, and establishing the connection between the local spatial neighborhood and the local spectrum fails to improve unmixing performance significantly. To address these limitations, a bilateral global attention network based on both spatial and spectral information is proposed. It enables the network to obtain respective feature dependencies in the two dimensions and achieve optimal fusion of both features. The network comprises two information extraction branches. The spatial information extraction branch uses the Swin Transformer block to acquire the global spatial attention of the overall image, while the spectral information extraction branch designates a simplified spectral channel attention mechanism to gain spectral attention weight maps. The network’s efficacy is demonstrated through a comparative study using a synthetic dataset and two real datasets. The code of this work is available at https://github.com/UPCGIT/SSABN. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
31. Automatic Coastline Extraction Based on the Improved Instantaneous Waterline Extraction Method and Correction Criteria Using SAR Imagery.
- Author
-
Zheng, Hongxia, Li, Xiao, Wan, Jianhua, Xu, Mingming, Liu, Shanwei, and Yasir, Muhammad
- Abstract
Coastlines with different morphologies form boundaries between the land and ocean, and play a vital role in tourism, integrated coastal zone management, and marine engineering. Therefore, determining how to extract the coastline from satellite images quickly, accurately, and intelligently without manual intervention has become a hot topic. However, the instantaneous waterline extracted directly from the image must be corrected to the coastline using the tide survey station data. This process is challenging due to the scarcity of tide stations. Therefore, an improved instantaneous waterline extraction method was proposed in this paper with an integrated Otsu threshold method, a region-growing algorithm, Canny edge detection, and a morphology operator. Based on SAR feature extraction and screening, the multi-scale segmentation method and KNN classification algorithms were used to achieve object-oriented automatic classification. According to different types of ground features, the correction criteria were presented and used in correcting the instantaneous waterline in biological coasts and undeveloped silty coasts. As a result, the accurate extraction of the coastline was accomplished in the area of the Yellow River Delta. The coastline was compared with that extracted from the GF-1 optical image. The result shows that the deviation degree was less than the field distance represented by three pixels. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
32. Remote Sensing Inversion of Typical Offshore Water Quality Parameter Concentration Based on Improved SVR Algorithm.
- Author
-
Ren, Jianghua, Cui, Jianyong, Dong, Wen, Xiao, Yanfang, Xu, Mingming, Liu, Shanwei, Wan, Jianhua, Li, Zhongwei, and Zhang, Jie
- Subjects
WATER quality ,OPTIMIZATION algorithms ,CHLOROPHYLL in water ,BODIES of water ,SPECTRAL sensitivity ,SPECTRAL reflectance ,REMOTE sensing ,CHLOROPHYLL spectra - Abstract
Chlorophyll a concentration and suspended matter concentration, as typical water quality parameters related to spectral characteristics, are essential for characterizing the degree of eutrophication in water bodies. They have become crucial indicators for water quality assessment of inland water bodies. The support vector regression model (SVR) is suitable for small samples, has excellent generalization ability, and has high prediction accuracy. Still, it has the problem of difficult selection of model parameters and quickly falling into local extremes. To solve this problem, a hybrid Differential Evolution-Grey Wolf Optimizer (DE-GWO) algorithm is introduced into the parameter selection process of the support vector regression model, and an improved SVR algorithm (DE-GWO-SVR) is proposed for the remote sensing inversion of chlorophyll a concentration and suspended sediment concentration in water bodies. In this paper, the spectral reflectance of the water surface and the chlorophyll a and broken matter concentration values were obtained by field measurements in the Tangdao Bay waters of Qingdao, Shandong Province. The inverse model between the concentration values of the two water quality parameters and the corresponding sensitive factors was established by first determining the sensitive factors based on the response of the spectral reflectance to the two water quality parameters and introducing the DE-GWO optimization algorithm into the parameter selection process of the SVR model. Finally, the accuracy of the model was verified using Sentinel II satellite remote sensing spectral data, and then the inverse accuracy of the two water quality parameters was obtained. The mean relative error (MRE) of the chlorophyll a prediction model built by the DE-GWO algorithm optimizing the SVR is 25.1%, and the mean relative error (MRE) of the suspended matter prediction model is 32.5%. The inversion results were all better than the other models (linear regression, SVR, and GWO-SVR model). When the best model, built from the measured water surface spectral data, was applied to the Sentinel II satellite data, the improved SVR model outperformed the other models in terms of mean relative error. The experimental results confirm that the DE-GWO-SVR algorithm is an effective method for remote sensing inversion of chlorophyll a and suspended matter concentrations in water bodies, which can provide a reference for remote sensing inversion of chlorophyll a and suspended matter concentrations in Chinese offshore waters and subsequent scientific management of waters. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
33. Quantitative Inversion Ability Analysis of Oil Film Thickness Using Bright Temperature Difference Based on Thermal Infrared Remote Sensing: A Ground-Based Simulation Experiment of Marine Oil Spill.
- Author
-
Wang, Meiqi, Yang, Junfang, Liu, Shanwei, Zhang, Jie, Ma, Yi, and Wan, Jianhua
- Subjects
OIL spills ,CONVOLUTIONAL neural networks ,REMOTE sensing ,PETROLEUM ,DEEP learning ,THICK films - Abstract
Oil spills on the sea surface have caused serious harm to the marine ecological environment and coastal environment. Oil film thickness (OFT) is an important parameter for estimating oil spills amount, and accurate quantification of OFT is of great significance for rapid response and risk assessment of oil spills. In recent years, thermal infrared remote sensing has been gradually applied to quantify the OFT. In this paper, the outdoor oil spill simulation experiments were designed, and the bright temperature (BT) data of different OFTs were obtained for 24 consecutive hours in summer and autumn. On the basis of the correlation analysis of OFT and bright temperature difference (BTD) between oil and water, the traditional regression fitting model, classical machine learning model, ensemble learning model, and deep learning model were applied to the inversion of OFT. At the same time, inversion results of the four models were compared and analyzed. In addition, the best OFT inversion time using thermal infrared was studied based on 24-h thermal infrared data. Additionally, the inversion results were compared with the measured results; the optimal OFT range detectable using thermal infrared was explored. The experimental results show that: (1) Compared with ensemble learning model, traditional regression fitting model, and classical machine learning model, Convolutional Neural Network (CNN) has the advantages of high stability while maintaining high-precision inversion, and can be used as the preferred model for oil film thickness inversion; (2) The optimal time for OFT detection is around 10:00 to 13:00 of the day, and is not affected by seasonal changes; (3) During the day, thermal infrared has good detection ability for OFT greater than 0.4 mm, and weak detection ability for thinner oil films; (4) At night, thermal infrared has certain detection ability for relatively thick oil film, but the accuracy is lower than that in the daytime. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. Estimation of Arctic sea ice thickness from CryoSat-2 altimetry data.
- Author
-
Jiang, Jinghui, Liu, Shanwei, Sun, Qintin, and Wan, Jianhua
- Subjects
- *
SEA ice , *STANDARD deviations , *CLIMATE change , *POLAR climate , *SNOW cover - Abstract
Arctic sea ice change is one of the critical factors affecting the global climate environment; hence, it is crucial to obtain sea ice thickness with high accuracy while studying polar and global climate change. In the process of using altimetry data to estimate sea ice thickness, the mean sea surface height will bring greater uncertainty to the extraction of sea ice freeboard, which will affect the accuracy of sea ice thickness. Also, the influence of snow cover on radar signal penetration will bring greater uncertainty to sea ice thickness. In this paper, we present a processing chain for sea ice thickness estimation. First, we compare the effect of four different MSS models on the freeboard estimation. Then, considering the incomplete penetration of radar signals and the different speeds of radar signals penetrating the snow layer and the vacuum, the traditional sea ice thickness model is optimized to obtain the sea ice thickness. Compared with the Operation IceBridge (OIB) sea ice thickness, the accuracy of sea ice thickness obtained by the optimized model is better than 0.350 m, with a root mean square error of 0.260 m and a mean bias of 0.048 m. The comparison results show that the combination of the latest DTU18 MSS model and sea ice thickness optimization model effectively improve the accuracy of sea ice thickness. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. Radiometric and Polarimetric Quality Validation of Gaofen-3 over a Five-Year Operation Period.
- Author
-
Yang, Le, Shi, Lei, Sun, Weidong, Yang, Jie, Li, Pingxiang, Li, Deren, Liu, Shanwei, and Zhao, Lingli
- Subjects
SYNTHETIC aperture radar ,NUCLEAR activation analysis ,RADARSAT satellites ,REMOTE-sensing images - Abstract
GaoFen-3 was the first Chinese civilian C-band synthetic aperture radar (SAR) satellite, launched in August 2016. The need for monitoring the satellite's image quality has been boosted by its widespread applications in various fields. The efficient and scientific assessment of the system's radiometric and polarimetric performance has been essential in its more than five years of service. The authors collected 90 images of the Inner Mongolia calibration site, 888 images of the Amazon rainforest, and 39,929 images of the Chinese mainland from 2017 to 2021. This was achieved whilst covering the leading imaging modes, such as the spotlight mode, stripmap mode, ultra-fine mode, wave imaging mode, etc. In this study, we derive a framework that incorporates the man-made corner reflectors (CRs) in Mongolia, the traditional Amazon rainforest datasets, and even the long-strip data in the Chinese mainland (known as CRAS) for the purposes of GaoFen-3 radiometric quality analysis and polarimetric validation over its five years of operation. Polarimetric calibration without recourse to the CRs is utilized to measure the polarimetric distortions regardless of the region, and thus requires a higher calibration accuracy for the GaoFen-3 polarimetric monitoring task. Consequently, the modified Quegan method is developed by relaxing the target azimuth symmetry constraint with the Amazon forest datasets. The experiments based on the CRAS demonstrate that the main radiometric characteristics could reach the international level, with an estimated noise-equivalent sigma zero of approximately −30 dB, a radiometric resolution that is better than 2.9 dB, and a single-imagery relative radiation accuracy that is better than 0.51 dB. For polarimetric validation, the modified Quegan method was utilized to measure the crosstalk for quad-pol products to ensure that it was than −40 dB. Meanwhile, non-negligible channel imbalance errors were found in the QPSII and WAV modes, and they were effectively well-calibrated with strip estimators to satisfy the system design. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
36. A Multilevel Spatial and Spectral Feature Extraction Network for Marine Oil Spill Monitoring Using Airborne Hyperspectral Image.
- Author
-
Wang, Jian, Li, Zhongwei, Yang, Junfang, Liu, Shanwei, Zhang, Jie, and Li, Shibao
- Subjects
OIL spills ,CONVOLUTIONAL neural networks ,FEATURE extraction ,MULTISPECTRAL imaging ,ARTIFICIAL neural networks ,REMOTE sensing - Abstract
Marine oil spills can cause serious damage to marine ecosystems and biological species, and the pollution is difficult to repair in the short term. Accurate oil type identification and oil thickness quantification are of great significance for marine oil spill emergency response and damage assessment. In recent years, hyperspectral remote sensing technology has become an effective means to monitor marine oil spills. The spectral and spatial features of oil spill images at different levels are different. To accurately identify oil spill types and quantify oil film thickness, and perform better extraction of spectral and spatial features, a multilevel spatial and spectral feature extraction network is proposed in this study. First, the graph convolutional neural network and graph attentional neural network models were used to extract spectral and spatial features in non-Euclidean space, respectively, and then the designed modules based on 2D expansion convolution, depth convolution, and point convolution were applied to extract feature information in Euclidean space; after that, a multilevel feature fusion method was developed to fuse the obtained spatial and spectral features in Euclidean space in a complementary way to obtain multilevel features. Finally, the multilevel features were fused at the feature level to obtain the oil spill information. The experimental results show that compared with CGCNN, SSRN, and A2S2KResNet algorithms, the accuracy of oil type identification and oil film thickness classification of the proposed method in this paper is improved by 12.82%, 0.06%, and 0.08% and 2.23%, 0.69%, and 0.47%, respectively, which proves that the method in this paper can effectively extract oil spill information and identify different oil spill types and different oil film thicknesses. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images.
- Author
-
Kong, Weimin, Liu, Shanwei, Xu, Mingming, Yasir, Muhammad, Wang, Dawei, and Liu, Wantao
- Subjects
- *
REMOTE-sensing images , *RESEARCH vessels , *COMPUTING platforms , *RADARSAT satellites , *FEATURE extraction , *REMOTE sensing - Abstract
As ship target detection technology has high application value in military and civil fields, it is significant to research ship detection in SAR images. Aiming at the complex and diverse backgrounds, significant differences in ship sizes, and real-time detection problems in the ship target detection task of SAR remote sensing images, a lightweight ship detection network based on the YOLOx-Tiny model is proposed. Firstly, a multi-scale ship feature extraction module is proposed, composed of a parallel multi-branch structure connected by a standard convolution layer, asymmetric convolution layer, and dilatation convolution layer with different expansion rates in turn. It makes better use of local features and global features and effectively improves the detection accuracy of multi-scale ship targets; Secondly, to ensure detection performance and eliminate background interference, we propose a whole SAR remote sensing image detection strategy based on an adaptive threshold, which effectively suppresses false alarms caused by background and improves detection speed. The experimental results on two different SAR ship datasets, SSDD and HRSID, show that, compared with several advanced methods, the effectiveness and superiority of the method in this paper are verified, and excellent results are shown in the detection of the whole SAR remote-sensing image. It can provide effective theoretical and technical support for ship detection on platforms with limited computing resources and has good application prospects. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. Radiance consistency and deviation characteristics in the regions of overlap between three new-generation GEO meteorological satellites.
- Author
-
Yue, Yatao, Xiao, Yanfang, Ma, Yi, Liu, Rongjie, and Liu, Shanwei
- Subjects
METEOROLOGICAL satellites ,RADIANCE ,CLIMATE change forecasts ,CLIMATE change ,BRIGHTNESS temperature - Abstract
The combination of multi-source Geostationary Earth Orbit (GEO) satellites can realize simultaneous global observation with high frequency, which is significant to fine weather forecast and global climate change research. Here the radiance consistency and deviation characterization in the overlapping regions are analysed for three new generation GEO satellites, including the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite, the Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-17 (GOES-17) and the Advanced Geostationary Radiation Imager (AGRI) onboard FengYun satellite-4A (FY-4A). The result shows that the radiance has higher consistency for visible and near-infrared (VNIR) channels with correlation coefficient r > 0.98 than infrared (IR) channels with r > 0.90 in the overlapping regions of different GEO satellites. For FY-4A & Himawari-8, the average percentage deviation (APD) of VNIR channels is 5.54%–9.03%. The brightness temperature (BT) bias of IR channels is 1.16 K-2.50 K. For GOES-17 & Himawari-8, the APD of VNIR channels is 4.48%–6.71%, and the BT bias of IR channels is 0.96 K −2.76 K. Solar-viewing geometries significantly influence the radiance deviation in the over regions. The future work is to design a radiance fusion method and achieve cloud detection in ample space with high frequency by combining multi-source GEO satellites. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. Cloud Occlusion Probability Calculation Jointly Using Himawari-8 and CloudSat Satellite Data.
- Author
-
Chen, Xingfeng, Zhao, Limin, Ding, Haonan, Wang, Donghong, Li, Jiaguo, Cao, Chen, Zheng, Fengjie, Li, Zhiliang, Liu, Jun, and Liu, Shanwei
- Subjects
SCIENTIFIC observation ,FLIGHT planning (Aeronautics) ,FLIGHT testing ,PROBABILITY theory ,INTERNATIONAL airports - Abstract
Cloud occlusion is an important factor affecting flight safety and scientific observation. The calculation of Cloud Occlusion Probability (COP) is significant for the planning of the flight time and route of aircraft. Based on Himawari-8 and CloudSat satellite data, we propose a method to calculate the COP. The COP statistics were carried out on different distances in 12 directions 6 km above Beijing Capital International Airport (BCIA), at different heights and directions in the Haiyang aerostat production base, and at different times and seasons in Mount Qomolangma. It was found that the COP going in the southern direction from BCIA was greater than that in the northern direction by 0.67–3.12%, which is consistent with the climate conditions of Beijing. In Haiyang, the COP for several seasons in the direction of land was higher than in the direction of the ocean. The maximum COP for the 6 km altitude is 29.63% (summer) and the minimum COP is 7.59% (winter). The aerostat flight test can be conducted in the morning of winter and the direction of the ocean. The best scientific observation time for Mount Qomolangma is between 02:00 and 05:00 UTC in spring. With the increase in altitude, the COP gradually decreases. The research in this paper provides essential support for flight planning. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
40. FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG.
- Author
-
Ding, Haonan, Zhao, Limin, Liu, Shanwei, Chen, Xingfeng, de Leeuw, Gerrit, Wang, Fu, Zheng, Fengjie, Zhang, Yuhuan, Liu, Jun, Li, Jiaguo, She, Lu, Si, Yidan, and Gu, Xingfa
- Subjects
AEROSOLS ,ATMOSPHERIC aerosols ,GEOSTATIONARY satellites ,IMAGE sensors ,SPATIAL resolution - Abstract
The Advanced Geostationary Radiation Imager (AGRI) is one of the main imaging sensors on the Fengyun-4A (FY-4A) satellite. Due to the combination of high spatial and temporal resolution, the AGRI is suitable for continuously monitoring atmospheric aerosol. Existing studies only perform AOD retrieval on the dark target area of FY-4A/AGRI, and the full disk AOD retrieval is still under exploration. The Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) based on the Fully Connected Neural Network (FCNN) was used to retrieve FY-4A/AGRI full disk aerosol optical depth (AOD). The data from 111 ground-based Aerosol Robotic Network (AERONET) and Sun–Sky Radiometer Observation Network (SONET) sites were used to train the neural network, and the data from 28 other sites were used for independent validation. FY-4A/AGRI AOD data from 2017 to 2020 were validated over the full disk and three different surface types (vegetated areas, arid areas, and marine and coastal areas). For general validation, the AOD predicted by the application of NNAeroG to FY-4A/AGRI observations is consistent with the ground-based reference AOD data. The validation of the FY-4A/AGRI AOD versus the reference data set shows that the root-mean-square error (RMSE), mean absolute error (MAE), R squared (R
2 ), and percentage of data with errors within the expected error ± (0.05 + 15%) (EE15) are 0.237, 0.145, 0.733, and 58.7%, respectively. The AOD retrieval accuracy over vegetated areas is high but there is potential for improvement of the results over arid areas and marine and coastal areas. AOD retrieval results of FY-4A/AGRI were compared under fine and coarse modes. The retrieved AOD has low accuracy in coarse mode but is better in coarse–fine mixed mode and fine mode. The current AOD products over the ocean of NNAeroG-FY4A/AGRI are not recommended. Further development of algorithms for marine areas is expected to improve the full disk AOD retrieval accuracy. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
41. Monitoring of Discolored Trees Caused by Pine Wilt Disease Based on Unsupervised Learning with Decision Fusion Using UAV Images.
- Author
-
Wan, Jianhua, Wu, Lujuan, Zhang, Shuhua, Liu, Shanwei, Xu, Mingming, Sheng, Hui, and Cui, Jianyong
- Subjects
CONIFER wilt ,PLURALITY voting ,DRONE aircraft ,INFORMATION resources management - Abstract
Pine wilt disease (PWD) has caused severe damage to ecosystems worldwide. Monitoring PWD is urgent due to its rapid spread. Unsupervised methods are more suitable for the monitoring needs of PWD, as they have the advantages of being fast and not limited by samples. We propose an unsupervised method with decision fusion that combines adaptive threshold and Lab spatial clustering. The method avoids the sample problem, and fuses the strengths of different algorithms. First, the modified ExG-ExR index is proposed for adaptive threshold segmentation to obtain an initial result. Then, k-means and Fuzzy C-means in Lab color space are established for an iterative calculation to achieve two initial results. The final result is obtained from the three initial extraction results by the majority voting rule. Experimental results on unmanned aerial vehicle images in the Laoshan area of Qingdao show that this method has high accuracy and strong robustness, with the average accuracy and F1-score reaching 91.35% and 0.8373, respectively. The method can help provide helpful information for effective control and tactical management of PWD. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
42. Coastal Waveform Retracking for HY-2B Altimeter Data by Determining the Effective Trailing Edge and the Low Noise Leading Edge.
- Author
-
Hong, Zhiheng, Yang, Jungang, Liu, Shanwei, Jia, Yongjun, Fan, Chenqing, and Cui, Wei
- Subjects
ALTIMETERS ,REMOTE sensing ,NOISE ,ALTIMETRY - Abstract
As an important remote sensing technology, satellite altimetry provides a large amount of observations of sea surface height over the global ocean. In coastal areas, the accuracy of satellite altimetry data decreases greatly due to issues arise in the vicinity of land, related to poorer geophysical corrections and artifacts in the altimeter reflected signals linked to the presence of land within the instrument footprint. To improve the application of HY-2B altimetry data in coastal areas, this study proposes a coastal waveform retracking strategy for HY-2B altimetry mission, which depends on the effective trailing edge and the leading edge, which are less affected by coastal 'contamination', to retrieve accurate waveform information. The HY-2B pass 323 and pass 196 data are reprocessed, and the accuracy of the reprocessing results in the range of 0–40 km offshore is validated against the tide gauge data and compared with the HY-2B standard SGDR data. According to the analysis conclusion, the accuracy of the reprocessed data is higher than that of the SGDR data and has good performance within 15 km offshore. For the pass 323, the mean value of correlation coefficient and RMS of the reprocessed data against the corresponding tide gauge data are 0.893 and 45.1 cm, respectively, in the range within 0–15 km offshore, and are 0.86 and 33.6 cm, respectively, in the range beyond 15 km offshore. For the pass 196, the mean value of correlation coefficient and RMS of the reprocessed data against the corresponding tide gauge data in the range within 0–12 km offshore are 0.84 and 33.0 cm, respectively, and in the range within 0–5 km offshore to the island are 0.90 and 29.3 cm, respectively, and in the range beyond 5 km offshore to the island are 0.92 and 36.2 cm, respectively, which are all better than the corresponding values of the SGDR data, especially in the range closed to the land. The results indicate that the proposed coastal waveform retracking strategy for HY-2B altimetry greatly improves the quality of HY-2B altimetry data in coastal areas. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
43. Using GNSS-IR Snow Depth Estimation to Monitor the 2022 Early February Snowstorm over Southern China.
- Author
-
Zhang, Jie, Liu, Shanwei, Liang, Hong, Wan, Wei, Guo, Zhizhou, and Liu, Baojian
- Subjects
- *
SNOW accumulation , *SNOWSTORMS , *GLOBAL Positioning System - Abstract
Snow depth is an essential meteorological indicator for monitoring snow disasters. The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has been proven to be a practical approach to retrieving snow depth. This study presents a case study to explore utilizing the GNSS-IR-derived snow depth to monitor the 2022 early February snowstorm over southern China. A snow depth retrieval framework considering data quality control and specific ground surface substances was developed using 8-day data from 13 operational GNSS/Meteorology stations. The daily snow depths retrieved from different ground surfaces, i.e., dry grass, wet grass, and concrete, agreed well with the measured snow depth, with Mean Absolute Error (MAE) of 2.79 cm, 3.36 cm, and 2.53 cm, respectively. The percentage MAE when snow depths > 5 cm for the three ground surface substances was 26.8%, 53.7%, and 35.0%, respectively. The 6 h snow depth results also showed a swift and significant response to the snowfall event. This study proves the potential of GNSS-IR, used as a new operational tool in the automatic meteorological system, to monitor snow disasters over southern China, particularly as an efficient and cost-effective framework for real-time and accurate monitoring. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
44. Spatio-temporal fire detection based on brightness temperature change in Himawari-8 images.
- Author
-
Zhang, Cunhui, Wan, Jianhua, Xu, Mingming, Liu, Shanwei, and Sheng, Hui
- Subjects
BRIGHTNESS temperature ,FIRE detectors ,GEOSTATIONARY satellites ,LEAST squares ,COMPARATIVE method ,STANDARD deviations - Abstract
The geostationary satellite Himawari-8 has become the primary source for fire remote-sensing detection because of its advantages of high frequency, large width and easy access. To solve the problem of difficulty in small fire detection and with a high omission on Himawari-8 image, a spatio-temporal fire detection (STFD) method based on Himawari-8 image brightness temperature change is proposed. Calculate the mean value difference of brightness temperature between multi-time series images before the target time, and use the least square model to fit the ideal mean value difference of brightness temperature between the target time and the image at the previous time. Then, the difference value of the actual brightness temperature between the target time and the last image time is calculated. The initial brightness temperature change pixels are obtained by the comparative analysis method. Based on the spatial statistical characteristics, the potential fire point is determined by the brightness temperature value ratio of the image, temperature mean and standard temperature deviation. Then, combined with the spatio-temporal context information, the persistent fire points are detected according to the threshold conditions to supplement undetected persistent fires. The results of experiments on the Himawari-8 images in Guizhou, China and southwestern Australia indicate that STFD exhibits superior performance on small fire detection points, and the accuracy is better than the several traditional methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
45. Combining low-rank constraint for similar superpixels and total variation sparse unmixing for hyperspectral image.
- Author
-
Ye, Chuanlong, Liu, Shanwei, Xu, Mingming, and Yang, Zhiru
- Subjects
- *
REMOTE sensing , *MATHEMATICAL optimization , *PIXELS , *PROBLEM solving - Abstract
Mixed pixels are the main reason for the low accuracy of traditional remote sensing applications. Hyperspectral image unmixing can explore the sub-pixel information of mixed pixels, which is an effective measure to solve the problems of low precision. Current unmixing methods only consider the correlation of local or global pixels and do not fully utilize images information. Based on the above problems, we proposed a novel sparse unmixing model named combining low-rank constrain for similar superpixels and total variation sparse unmixing (CLRSS-TV) in this current research paper. The adjacent similar pixels are clustered first into superpixels, then the superpixels with the same semantic information are merged into a collection. The spatial information of the image is fully considered by twice clustering. Weighted low-rank constraint is imposed on each collection to consider the spectral correlation of pixels. Moreover, the TV regularization and l2,1 norm are utilized to improve the smoothness and row sparsity, respectively. Experimental results reveal that the proposed method is not only competitive with the four state-of-the-art algorithms in simulated and actual datasets, but also has good anti-noise ability and universality. However, this paper still fails to overcome the problem of parameters selection in sparse unmixing, so the future work study intends to adopt the intelligent optimization algorithm to obtain the optimal parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
46. Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data.
- Author
-
Liu, Shanwei, Jiao, Yue, Sun, Qinting, and Jiang, Jinghui
- Subjects
- *
SEA level , *BOX-Jenkins forecasting , *LONG-term memory , *RADIAL basis functions , *MINERAL oils , *TREND analysis - Abstract
The South China Sea is China's largest marginal sea area, and it is rich in oil and gas mineral resources; thus, estimating its sea level changes is of practical significance. Based on linear and nonlinear sea level change characteristics, this paper decomposes 1992–2019 monthly mean sea level anomaly time series in the South China Sea into trend, seasonal, and random terms. This paper compares the seasonal autoregressive integrated moving average (SARIMA) and Prophet models for estimating the trend and seasonal terms and the long short-term memory (LSTM) and radial basis function (RBF) models for estimating random terms, and the more suitable models were selected. A Prophet-LSTM combined model was developed based on the accuracy results. This paper uses the combined model to study the effect of known data length on the experimental results and determines the best prediction duration. The results show that the combined model is suitable for short-term and medium-term estimations of 12–36 months. The accuracy at 36 months is 0.962 cm, which proves that the combined model has high application value for estimating sea level changes in the South China Sea. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
47. THE RESEARCH ON THE SPECTRAL CHARACTERISTICS OF SEA FOG BASED ON CALIOP AND MODIS DATA.
- Author
-
Wan Jianhua, Su Jing, Liu Shanwei, and Sheng Hui
- Subjects
FOG ,LIDAR ,MODIS (Spectroradiometer) - Abstract
In view of that difficulty of distinguish between sea fog and low cloud by optical remote sensing mean, the research on spect ral characteristics of sea fog is focused and carried out? The satellite laser radar CALIOP data and the high spectral MODIS data were obtained from May to December 2017, and the scattering coefficient and the vertical height information were extracted from the atmospheric attenuation of the lower star to extract the sea fog sample points, and the spectral response curve based on MODIS was formed to analyse the spectral response characteristics of the sea fog, thus providing a theoretical basis for the monitoring of sea fog with optical remote sensing image. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
48. THE RESEARCH ON ELEVATION CHANGE OF ANTARCTIC ICE SHEET BASED ON CRYOSAT-2 ALIMETER DATA.
- Author
-
Sun Qinting, Wan Jianhua, Liu Shanwei, and Li Yinlong
- Subjects
ALTIMETERS ,ENVIRONMENTAL research - Abstract
In this paper, the Cryosat-2 altimeter data distributed by the ESA, and these data are processed to extract the information of the elevation change of the Antarctic ice sheet from 2010 to 2017.Firstly, the main pretreatment preprocessing for Cryosat-2 altimetry data is crossover adjustment and elimination of rough difference. Then the grid DEM of the Antarctic ice sheet was constructed by using the kriging interpolation method, and analyzed the spatial characteristic time characteristics of the Antarctic ice sheet. The latitude-weighted elevation can be obtained by using the elevation data of each cycle, and then the general trend of the Antarctic ice sheet elevation variation can be seen roughly. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
49. Assessment of the Greenland ice sheet change (2011–2021) derived from CryoSat-2.
- Author
-
Liu, Shanwei, Jiang, Jinghui, Sun, Qinting, Wan, Jianhua, and Sheng, Hui
- Subjects
GREENLAND ice ,ICE sheets ,ICE sheet thawing ,MELTWATER ,SEA level - Abstract
The Greenland ice sheet melting status is critical for global sea level rise and climate change. Based on the CryoSat-2 altimetry data from 2011 to 2021, the 5 km × 5 km DEMs of the Greenland ice sheet were derived by adopting the kriging interpolation method. Then the changes in elevation and volume of the ice sheet were calculated by using the intersection method. The changes in the ice sheet were analysed, and the results show that: (1) The accuracy of the Greenland DEMs obtained based on satellite altimetry data is region-dependent, with better accuracy in inland areas and higher elevation errors in marginal areas. (2) The inland area elevation remains unchanged basically or even shows an increasing trend, and there is an apparent melting trend in the marginal area, especially on the west coast. The contribution of the ice sheet melting mainly comes from elevations below 2000 m. (3) The main body of the Greenland ice sheet is melting with an elevation change rate of −13.27 ± 0.86 cm·a
−1 and a volume change rate of −202.47 ± 14.8 km³·a−1 . The rate of the Greenland ice sheet thinning has slowed down compared with the changes from 2003 to 2009. [ABSTRACT FROM AUTHOR]- Published
- 2023
- Full Text
- View/download PDF
50. Multi-Scale Ship Detection Algorithm Based on a Lightweight Neural Network for Spaceborne SAR Images.
- Author
-
Liu, Shanwei, Kong, Weimin, Chen, Xingfeng, Xu, Mingming, Yasir, Muhammad, Zhao, Limin, and Li, Jiaguo
- Subjects
- *
SYNTHETIC aperture radar , *FEATURE extraction , *SHIPS , *ALGORITHMS - Abstract
The current limited spaceborne hardware resources and the diversity of ship target scales in SAR images have led to the requirement of on-orbit real-time detection of ship targets in spaceborne synthetic aperture radar (SAR) images. In this paper, we propose a lightweight ship detection network based on the YOLOv4-LITE model. In order to facilitate the network migration to the satellite, the method uses MobileNetv2 as the backbone feature extraction network of the model. To solve the problem of ship target scale diversity in SAR images, an improved receptive field block (RFB) structure is introduced, enhancing the feature extraction ability of the network, and improving the accuracy of multi-scale ship target detection. A sliding window block method is designed to detect the whole SAR image, which can solve the problem of image input. Experiments on the SAR ship dataset SSDD show that the detection speed of the improved lightweight network could reach up to 47.16 FPS, with the mean average precision (mAP) of 95.03%, and the model size is only 49.34 M, which demonstrates that the proposed network can accurately and quickly detect ship targets. The proposed network model can provide a reference for constructing a spaceborne real-time lightweight ship detection network, which can balance the detection accuracy and speed of the network. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.