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In the field of remote sensing image processing, pansharpening technology stands as a critical advancement. This technology aims to enhance multispectral images that possess low resolution by integrating them with high-spatial-resolution panchromatic images, ultimately producing multispectral images with high resolution that are abundant in both spatial and spectral details. Thus, there remains potential for improving the quality of both the spectral and spatial domains of the fused images based on deep-learning-based pansharpening methods. This work proposes a new method for the task of pansharpening: the Multi-Stage Progressive Pansharpening Network with Detail Injection with Redundancy Reduction Mechanism (MSPPN-DIRRM). This network is divided into three levels, each of which is optimized for the extraction of spectral and spatial data at different scales. Particular spectral feature and spatial detail extraction modules are used at each stage. Moreover, a new image reconstruction module named the DRRM is introduced in this work; it eliminates both spatial and channel redundancy and improves the fusion quality. The effectiveness of the proposed model is further supported by experimental results using both simulated data and real data from the QuickBird, GaoFen1, and WorldView2 satellites; these results show that the proposed model outperforms deep-learning-based methods in both visual and quantitative assessments. Among various evaluation metrics, performance improves by 0.92–18.7% compared to the latest methods. [ABSTRACT FROM AUTHOR]

Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality. [ABSTRACT FROM AUTHOR]

The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods. [ABSTRACT FROM AUTHOR]

In maritime transportation, a ship's draft survey serves as a primary method for weighing bulk cargo. The accuracy of the ship's draft reading determines the fairness of bulk cargo transactions. Human visual-based draft reading methods face issues such as safety concerns, high labor costs, and subjective interpretation. Therefore, some image processing methods are utilized to achieve automatic draft reading. However, due to the limitations in the spectral characteristics of RGB images, existing image processing methods are susceptible to water surface environmental interference, such as reflections. To solve this issue, we obtained and annotated 524 multispectral images of a ship's draft as the research dataset, marking the first application of integrating NIR information and RGB images for automatic draft reading tasks. Additionally, a dual-branch backbone named BIF is proposed to extract and combine spectral information from RGB and NIR images. The backbone network can be combined with the existing segmentation head and detection head to perform waterline segmentation and draft detection. By replacing the original ResNet-50 backbone of YOLOv8, we reached a mAP of 99.2% in the draft detection task. Similarly, combining UPerNet with our dual-branch backbone, the mIoU of the waterline segmentation task was improved from 98.9% to 99.3%. The inaccuracy of the draft reading is less than ±0.01 m, confirming the efficacy of our method for automatic draft reading tasks. [ABSTRACT FROM AUTHOR]

This study is structured to address the problem of mapping the bottom of shallow and ultra-shallow inland water bodies using high-resolution satellite imagery. These environments, with their diverse distribution of optically relevant components, pose a challenge to traditional mapping methods. The study was conducted on several research issues, each focusing on a specific aspect of the SDB, related to the selection of spectral bands and regression models, regression models creation, evaluation of the influence of the number and spatial distribution of reference soundings, and assessment of the quality of the bathymetric surface, with a focus on microtopography. The study utilized basic empirical techniques, incorporating high-precision reference data acquired via an unmanned surface vessel (USV) integrated with a single-beam echosounder (SBES), and Global Navigation Satellite System (GNSS) receiver measurements. The performed investigation allowed the optimization of a methodology for bathymetry acquisition of such areas by identifying the impact of individual processing components. The first results indicated the usefulness of the proposed approach, which can be confirmed by the values of the obtained RMS errors of elaborated bathymetric surfaces in the range of up to several centimeters in some study cases. The work also points to the problematic nature of this type of study, which can contribute to further research into the application of remote sensing techniques for bathymetry, especially during acquisition in optically complex waters. [ABSTRACT FROM AUTHOR]

Introduction: This research explores the application of generative artificial intelligence, specifically the novel ARISGAN framework, for generating highresolution synthetic satellite imagery in the challenging arctic environment. Realistic and high-resolution surface imagery in the Arctic is crucial for applications ranging from satellite retrieval systems to the wellbeing and safety of Inuit populations relying on detailed surface observations. Methods: The ARISGAN framework was designed by combining dense block, multireceptive field, and Pix2Pix architecture. This innovative combination aims to address the need for high-quality imagery and improve upon existing state-of-the-art models. Various tasks and metrics were employed to evaluate the performance of ARISGAN, with particular attention to land-based and sea icebased imagery. Results: The results demonstrate that the ARISGAN framework surpasses existing state-of-the-art models across diverse tasks and metrics. Specifically, land-based imagery super-resolution exhibits superior metrics compared to sea ice-based imagery when evaluated across multiple models. These findings confirm the ARISGAN framework's effectiveness in generating perceptually valid highresolution arctic surface imagery. Discussion: This study contributes to the advancement of Earth Observation in polar regions by introducing a framework that combines advanced image processing techniques with a well-designed architecture. The ARISGAN framework's ability to outperform existing models underscores its potential. Identified limitations include challenges in temporal synchronicity, multispectral image analysis, preprocessing, and quality metrics. The discussion also highlights potential avenues for future research, encouraging further refinement of the ARISGAN framework to enhance the quality and availability of high-resolution satellite imagery in the Arctic. [ABSTRACT FROM AUTHOR]

Hyperspectral images can be widely used in many fields due to their high information richness. However, costly and complex imaging spectrometers limit its growth. Hyperspectral reconstruction aims to obtain the corresponding hyperspectral image from the multispectral image, to reduce the acquisition cost of hyperspectral images. At present, the related works mainly use methods based on deep learning, and they have achieved good results. However, how to fully extract the global spectral and spatial features of hyperspectral images is still not well solved. To address these issues, we propose an Attention and Transformer Complementary Fusion Network (ATCFNet), which is composed of three modules: Multi-angle Input Image Processing (MIIP), Deep Feature Extraction (DFE) and Hyperspectral Reconstruction (HR) modules. Within the DFE module, an improved Transformer module and a novel Multi-scale Spatial Attention (MSA) module are proposed to extract the global spectral relationship and the spatial features of the hyperspectral images, respectively. Moreover, the MIIP module is proposed to extract the features of input multispectral images more effectively and comprehensively. To verify these, we compare the proposed ATCFNet with other excellent reconstruction methods on three hyperspectral image datasets. The results show that our method achieves the best results in these datasets. Code is publicly available at . [ABSTRACT FROM AUTHOR]

The implementation of tumor grading tasks with image processing and machine learning techniques has progressed immensely over the past several years. Multispectral imaging enabled us to capture the sample as a set of image bands corresponding to different wavelengths in the visible and infrared spectrums. The higher dimensional image data can be well exploited to deliver a range of discriminative features to support the tumor grading application. This paper compares the classification accuracy of RGB and multispectral images, using a case study on colorectal tumor grading with the QU-Al Ahli Dataset (dataset I). Rotation-invariant local phase quantization (LPQ) features with an SVM classifier resulted in 80% accuracy for the RGB images compared to 86% accuracy with the multispectral images in dataset I. However, the higher dimensionality elevates the processing time. We propose a band-selection strategy using mutual information between image bands. This process eliminates redundant bands and increases classification accuracy. The results show that our band-selection method provides better results than normal RGB and multispectral methods. The band-selection algorithm was also tested on another colorectal tumor dataset, the Texas University Dataset (dataset II), to further validate the results. The proposed method demonstrates an accuracy of more than 94% with 10 bands, compared to using the whole set of 16 multispectral bands. Our research emphasizes the advantages of multispectral imaging over the RGB imaging approach and proposes a band-selection method to address the higher computational demands of multispectral imaging. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the American Institute for Conservation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Colorimetric color reproduction of different multispectral imaging systems applied to cultural heritage studies were examined. • Two methods were devised named Color Transformation Method and Illuminant Method. • The Color Transformation Method is more meticulous and leads to a better result as opposed to the Illuminant Method. • The Illuminant Method is much simpler and still leads to acceptable results. • The two methods have been shown to be applicable to both cameras showing the applicability of the methods developed here to different multispectral imaging systems. Multispectral imaging has long been used in the study of cultural heritage artifacts, particularly manuscripts with low-contrast text. The goal of multispectral imaging systems has largely been to enhance the text by imaging outside the spectral response of the human visual system and by applying various image processing techniques. However, there is a desire to use multispectral images for high quality, true color rendering of the artifacts as well. We use two multispectral imaging systems in this work. One of them is the Multispectral Imaging System for the study of Historical Artifacts (MISHA) which has been demonstrated to have significant utility at a lower cost and better ease of use compared to commercially available systems. This system was specifically designed and built for the purpose of imaging artifacts, particularly manuscripts and works on paper with illegible text due to deterioration and other environmental causes. Given this goal of "discovering" low-contrast text (and other features) on planar objects, the system has not been optimized for color reproduction purposes. The second system is a commercially available MegaVision EVTM Spectral Imaging System. This system is also used for cultural heritage imaging. In this work, we develop an approach to colorimetrically characterize these cameras and illumination systems, adding an option for accurate color reproduction to their capabilities. To do this, we present two different approaches. The first approach we call the Color Transformation Method. Here we use a Macbeth ColorChecker to first linearize the camera photometric response, and then find a linear matrix transformation from the camera response to CIEXYZ tristimulus values. The second approach we call the Illuminant Method. Here we consider each output by the camera as a reflectance factor value at the known wavelength of a narrowband illuminator. We then convert these values to the color of the object in terms of CIEXYZ tristimulus values. We show that the Color Transformation Method using a Macbeth ColorChecker leads to a more accurate result. Meanwhile, the Illuminant Method is much simpler and produces acceptable results, particularly considering accurate color reproduction was not a goal in designing the MISHA camera system. The same methods are applied to both imaging systems to show the robustness and applicability of the approaches developed herein to other imaging systems. Results are shown for both quantitative calibration targets as well as artifacts of interest to the field of cultural heritage studies. It is worth noting that we are not comparing the two imaging systems overall or the color processing software available from MegaVision. Rather, we simply show that the two approaches developed here are applicable to different multispectral imaging systems, from a very cost-effective system, i.e., MISHA to a more expensive one, i.e., MegaVision. [ABSTRACT FROM AUTHOR]

In multi-spectral images made by Earth observation satellites that use push-broom scanning, such as those operated by Planet Labs Corp., moving objects can be identified by the appearance of the object at different locations in each spectral band. The apparent velocity can be measured if the relative acquisition time between images in different spectral bands is known to millisecond accuracy. The images in the Planet Labs archive are mosaics of individual exposures acquired at different times. Thus, there is not a unique acquisition time for each spectral band. In an earlier paper, we proposed a method to determine the relative acquisition times from the information in the images themselves. High-altitude balloons provide excellent targets to test our proposed method because of their high apparent velocity due to the orbital velocity of the satellite and geometric parallax in images aligned to the level of the ground. We use images of the Chinese balloon that crossed the US in February 2024 as well as images of an identical balloon over Colombia to test our method. Our proposed method appears to be successful and allows the measurement of the apparent velocity of moving objects from the information available in the archive. [ABSTRACT FROM AUTHOR]

Nitrogen is the most important nutritional element during the vegetative growth phase of the pineapple crop; however, its presence in the soil is insufficient to meet plant demands. In this study, nine machine learning techniques were validated to estimate the total nitrogen (TN) content in MD2 pineapple crops from data from multiple sources. These sources included multispectral images captured by an unmanned aerial vehicle (UAV); in situ sensors, which collected information on ecological factors such as pH, temperature, solar radiation, relative humidity, soil moisture, wind speed and direction, as well as SPAD values indicating leaf chlorophyll content.Total nitrogen (TN) values were taken from leaf tissue samples, which were then analyzed in a laboratory. To introduce nitrogen variability, a complete randomized block experimental design was implemented, applying five different treatments in five blocks, each with 12 replications, during a 6-month period in a pineapple crop located in Tauramena, Colombia. To address the inherent variability in the agricultural and environmental data, dimensionality was reduced using Principal Component Analysis (PCA). In addition, regularization techniques were applied, including cross-validation, feature selection, boost methods, L1 (Lasso) and L2 (Ridge) regularization, as well as hyperparameter optimization. These strategies generated more robust and accurate models, with the multilayer perceptron regressor (MLP regressor) and extreme gradient boosting (XGBoost) algorithms standing out. On the first sampling date, XGBoost achieved an R2 of 86.98 %, being the highest. On the following dates, MLP achieved a R2 of 59.11 % on the second date; XGBoost achieved a R2 of 68.00 % on the third date, and on the last date, MLP achieved a R2 of 69.4 %. These results indicate that the integration of data from multiple sources and the use of machine learning models could greatly improve the precision of nitro-gen (N) diagnostics in pineapple crops, especially in real-time applications. These findings highlight the promising potential of developing machine learning models that integrate multisensor data fusion for various applications in agriculture.

• Multispectral imaging for study of the Sarajevo Haggadah. • Non-invasive scientific analysis of medieval illuminated Hebrew manuscripts. • Application of image processing techniques to recover erased text. Over the last two decades, multispectral imaging (MSI) has established itself as the most important tool in recovering illegible text in erased or damaged manuscripts, and to a lesser extent as an aid to the codicological study of manuscript supports and media. Rarely, however, have spectral imaging initiatives involved Hebrew illuminated manuscripts. In 2022, an international team of scholars, imaging scientists, and conservators from the National Museum of Bosnia and Herzegovina and the Lazarus Project of the University of Rochester imaged the Sarajevo Haggadah multispectrally. Produced for a Jewish commissioner and housed at the National Museum of Bosnia and Herzegovina, this 14th-century illuminated manuscript contains, inter alia , a partially erased text of a sale contract on folio 106v* that is now newly legible. Codicological details of the manuscript – ink, stains, type of animal used for parchment – have also benefitted from material analysis via MSI. Herein, we describe the MSI process of the Sarajevo Haggadah and the application of image processing algorithms to recover the erased text on folio 106v*. A complete text of a sale contract is revealed for the first time, providing significant information about the ownership and provenance of the Sarajevo Haggadah at the beginning of the 16th century. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality.

The purpose of pan-sharpening is to generate high-resolution multispectral (HRMS) images by combining multispectral images (MS) and panchromatic images (PAN), which have become an important part of remote sensing image processing. Therefore, how to better extract complete feature information from MS images and PAN images has become the focus of our attention. In this paper, we propose a new pansharpening method, called Transformer-based Dual-path cross fusion network (TDF) for Pan-sharpening remote sensing images, which aims to extract the spatial details of PAN images while maintaining the spectral fidelity of MS images. The whole network structure can be divided into two parts: in the encoder part, we adopt the Swin-Transformer module for the downsampling operation, which expands the sensory field of the network to the feature map, and then extracts the global information. However, since the Swin-Transformer module is not good at extracting pixel-level details, we introduce the Base Feature Extraction (BFE) and the Invertible Neural Network Block (INNB) modules for the interaction between the local and the global feature information. we also introduce the Edge-Enhancement Block (EEB) to further enhance the feature extraction at multi-scales during the image fusion process. In the decoder section, we once again employ the Swin Transformer module for downsampling. After the convolution operation and activation function, we utilize the Sub-Pixel Convolutional Neural Network for upsampling to generate the ultimate high-resolution multispectral images. Simulation experiments and real experiments are conducted on QuickBird (QB) and WorldView2 (WV2) datasets, which demonstrated our method are superior to the current methods. [ABSTRACT FROM AUTHOR]

Multispectral object detection is a crucial technology in remote sensing image processing, particularly in low-light environments. Most current methods extract features at a single scale, resulting in the fusion of invalid features and the failure to detect small objects. To address these issues, we propose a multispectral object detection network based on multilevel feature fusion and dual feature modulation (GMD-YOLO). Firstly, a novel dual-channel CSPDarknet53 network is used to extract deep features from visible-infrared images. This network incorporates a Ghost module, which generates additional feature maps through a series of linear operations, achieving a balance between accuracy and speed. Secondly, the multilevel feature fusion (MLF) module is designed to utilize cross-modal information through the construction of hierarchical residual connections. This approach strengthens the complementarity between different modalities, allowing the network to improve multiscale representation capabilities at a more refined granularity level. Finally, a dual feature modulation (DFM) decoupling head is introduced to enhance small object detection. This decoupled head effectively meets the distinct requirements of classification and localization tasks. GMD-YOLO is validated on three public visible-infrared datasets: DroneVehicle, KAIST, and LLVIP. DroneVehicle and LLVIP achieved mAP@0.5 of 78.0% and 98.0%, outperforming baseline methods by 3.6% and 4.4%, respectively. KAIST exhibited an MR of 7.73% with an FPS of 61.7. Experimental results demonstrated that our method surpasses existing advanced methods and exhibits strong robustness. [ABSTRACT FROM AUTHOR]

Hyperspectral classification is a task of significant importance in the field of remote sensing image processing, with attaining high precision and rapid classification increasingly becoming a research focus. The classification accuracy depends on the degree of raw HSI feature extraction, and the use of endless classification methods has led to an increase in computational complexity. To achieve high accuracy and fast classification, this study analyzes the inherent features of HSI and proposes a novel spectral–spatial feature extraction method called window shape adaptive singular spectrum analysis (WSA-SSA) to reduce the computational complexity of feature extraction. This method combines similar pixels in the neighborhood to reconstruct every pixel in the window, and the main steps are as follows: rearranging the spectral vectors in the irregularly shaped region, constructing an extended trajectory matrix, and extracting the local spatial and spectral information while removing the noise. The results indicate that, given the small sample sizes in the Indian Pines dataset, the Pavia University dataset, and the Salinas dataset, the proposed algorithm achieves classification accuracies of 97.56%, 98.34%, and 99.77%, respectively. The classification speed is more than ten times better than that of other methods, and a classification time of only about 1–2 s is needed. [ABSTRACT FROM AUTHOR]

Population growth has led to an exponential demand for agricultural products, to meet this demand it is necessary to improve management and achieve efficient use of resources without compromising the sustainability of ecosystems, particularly agricultural ones. One of the technologies that facilitate these tasks is precision agriculture (PA), which focuses on the optimization of resources and inputs based on the compilation of precise and timely geo-information of variables of agricultural interest with high spatio-temporal variability, obtained through remote sensors of three types: images captured by satellites or airplanes, images obtained from manned and unmanned aerial vehicles (UAVs) and specific information with sensors mounted on machinery or in the field. These limitations were overcome by using multispectral images, which has increased applications for agricultural purposes. Currently, multispectral images allow quantifying soil moisture, monitoring the presence of droughts and the degree of crop water stress, estimating the temporal and spatial variability of evapotranspiration, monitoring phenology, detecting nutritional deficiencies, estimating the degree of weed infestation. and insects, calculate organic carbon and soil salinity, and estimate yields and agricultural production. The use of geospatial technologies in the PA has changed the paradigm of agriculture and today constitutes a viable alternative to face the challenges that food production demands in a world with high climate variability. [ABSTRACT FROM AUTHOR]

Exploration of the effectiveness of multi-type features and multi-seasonal data of remote sensing images and selection of an optimal feature set from all extracted features are popular research topics in tree species classification. Eight typical image feature sets, namely, spectral band, digital surface model (DSM), texture (TEX), tassel cap transformation (TC), hue, saturation and value colour space (HSV), principal component analysis, minimum noise fraction (MNF) and spectral index (SI), were extracted in this study from images of four seasons acquired using the RedEdge-MX sensor, and maximum likelihood and random forest classifiers were used to categorise 32 typical urban tree species. Experimental results revealed the following: (1) the tree species recognition accuracy determined using the texture set (87.89%) was higher than that determined using other types of feature sets; (2) the optimal feature set containing 20 features comprised 4 DSMs, 11 TEXs, 2 TCs, 1 HSV (S), 1 SI and 1 MNF, and the classification accuracy determined using the set of features was 89.53% and (3) the classification accuracy for tree species identification determined using multi-seasonal spectral data was higher than that determined using individual seasonal data. The major contribution of this study to relevant literature is that it proves that urban greening tree species can be accurately identified using multiple features and seasonal images acquired through UAV-based sensors. The multi-feature-based approach also performs substantially well in practical applications for mapping tree species in a general urban environment considering the effects of a heterogeneous environment on tree species classification and comprehensive image processing and classification methods. [ABSTRACT FROM AUTHOR]

This study develops a novel approach for identifying buildings that were damaged in the aftermath of the Kahramanmaraş earthquakes on February 6, 2023, which were among the most devastating in the history of Türkiye. The approach involves using two pre-event and one post-event Sentinel-1 and Sentinel-2 images to detect changes in the varying-sized and shaped buildings following the earthquakes. The approach is based on the hypothesis that the radiometric characteristics of building pixels should change after an earthquake, and these changes can be detected by analysing the spectral distance between the building pixel vectors before and after the earthquake. The proposed approach examines the changes in building pixel vectors on pre-event and post-event Sentinel-2 MultiSpectral Instrument images. It also incorporates the backscattering features of Sentinel-1 Synthetic Aperture Radar images, as well as the variance image, a feature that is derived from a Grey-Level Co-occurrence Matrix, and the Normalized Difference Built-up Index image, which were derived from the optical data. The approach was tested on three sites, two of which were in Kahramanmaraş and the third in Hatay city. The results showed that the proposed method was able to accurately identify damaged and undamaged buildings with an overall accuracy of 75%, 84.4%, and 73.8% in test sites 1, 2, and 3, respectively. These findings demonstrate the potential of the proposed approach to effectively identify damaged buildings in post-disaster situations. [ABSTRACT FROM AUTHOR]

This article presents an integrated system that uses the capabilities of unmanned aerial vehicles (UAVs) to perform a comprehensive crop analysis, combining qualitative and quantitative evaluations for efficient agricultural management. A convolutional neural network-based model, Detectron2, serves as the foundation for detecting and segmenting objects of interest in acquired aerial images. This model was trained on a dataset prepared using the COCO format, which features a variety of annotated objects. The system architecture comprises a frontend and a backend component. The frontend facilitates user interaction and annotation of objects on multispectral images. The backend involves image loading, project management, polygon handling, and multispectral image processing. For qualitative analysis, users can delineate regions of interest using polygons, which are then subjected to analysis using the Normalized Difference Vegetation Index (NDVI) or Optimized Soil Adjusted Vegetation Index (OSAVI). For quantitative analysis, the system deploys a pre-trained model capable of object detection, allowing for the counting and localization of specific objects, with a focus on young lettuce crops. The prediction quality of the model has been calculated using the AP (Average Precision) metric. The trained neural network exhibited robust performance in detecting objects, even within small images. [ABSTRACT FROM AUTHOR]

Different types of surface defects will occur during the production of strip steel. To ensure production quality, it is essential to classify these defects. Our research indicates that two main problems exist in the existing strip steel surface defect classification methods: (1) they cannot solve the problem of unbalanced data using few-shot in reality, (2) they cannot meet the requirement of online real-time classification. To solve the aforementioned problems, a relational knowledge distillation self-adaptive residual shrinkage network (RKD-SARSN) is presented in this work. First, the data enhancement strategy of Cycle GAN defective sample migration is designed. Second, the self-adaptive residual shrinkage network (SARSN) is intended as the backbone network for feature extraction. An adaptive loss function based on accuracy and geometric mean (Gmean) is proposed to solve the problem of unbalanced samples. Finally, a relational knowledge distillation model (RKD) is proposed, and the functions of GUI operation interface encapsulation are designed by combining image processing technology. SARSN is used as a teacher model, its generalization performance is transferred to the lightweight network ResNet34, and it is conveniently deployed as a student model. The results show that the proposed method can improve the deployment efficiency of the model and ensure the real-time performance of the classification algorithms. It is superior to other mainstream algorithms for fine-grained images with unbalanced data classification. [ABSTRACT FROM AUTHOR]

NASA's Mars‐2020 Perseverance rover spent its first year in Jezero crater studying the mafic lava flows of the Máaz formation and the ultramafic cumulates of the Séítah formation, both of which have undergone minor alteration and are variably covered by coatings, dust, and/or soil deposits. Documenting the rock and soil characteristics across the crater floor is critical for establishing the geologic context of Perseverance's cached samples—which will eventually be returned to Earth—and for interpreting the deposition and modification of the Máaz and Séítah formations. Mastcam‐Z, a pair of multispectral, stereoscopic zoom‐lens cameras, provides broadband red/green/blue and narrowband visible to near‐infrared images (VNIR, 440–1,020 nm). From multispectral observations from sols 0 to 380, we compiled a database of ∼2,400 representative Mastcam‐Z spectra. We analyzed principal components, spectral parameters, and laboratory spectra of pure minerals and natural rock surfaces to interpret the spectral diversity of rocks and soils. We define eight spectral classes of rocks: Dusty, Hematite‐like, Coated, Low‐Ca Pyroxene‐like, Olivine‐like, Weathered Olivine‐like, Fe‐rich Pyroxene‐like, and Dark Oxide‐like. The variability of soil spectra in the Jezero crater floor is controlled primarily by the amount of dust and indicates a largely consistent soil mineralogy across the traverse, with the exception of the area disturbed by the landing event. In comparison to rock spectra from the Curiosity rover's Mastcam instrument in Gale crater, rocks on the Jezero crater floor are generally less spectrally diverse, but the Olivine‐like rocks within the Séítah formation represent a new spectral rock class in Mars surface exploration. Plain Language Summary: NASA's Mars‐2020 Perseverance rover spent its first year in Jezero crater studying rocks that formed in lava flows. These rocks were altered slightly by small amounts of water and are covered with dust and other coatings. Understanding these rocks, and the soils across Perseverance's traverse, is important for two reasons: Perseverance has collected rock and soil samples, which will be the first samples from Mars to be sent back to Earth, and they give insights into the history of Jezero crater. Here, we describe a database of spectra that we compiled from the Mastcam‐Z instrument, which is a pair of science cameras on Perseverance's mast. We analyzed ∼2,400 spectra representing the diversity of rocks and soils across the first 380 Martian days of the mission. We find that the amount of dust in the soils controls their spectral variability. We define eight classes of rock spectra, which are controlled by varying amounts of pyroxenes, olivines, hematite, and other oxides. Compared to spectra from the Mastcam instrument, which documented the Curiosity traverse in Gale crater, the Mastcam‐Z spectra are generally less diverse. However, the Mastcam‐Z spectra of olivine‐like rocks are new, as they have not been previously encountered on Mars. Key Points: We compiled a database of Mastcam‐Z spectra from the first 380 sols of Perseverance's mission in the floor of Jezero crater, MarsSoil spectral variability is controlled primarily by amounts of dust and indicates a largely consistent soil mineralogy across the traverseRock spectral variability is controlled by varying amounts of pyroxenes, olivines, hematite, and other oxides [ABSTRACT FROM AUTHOR]

With the continuous advancement of remote sensing technology, the information encapsulated within hyperspectral images has become increasingly enriched. The effective and comprehensive utilization of spatial and spectral information to achieve the accurate classification of hyperspectral images presents a significant challenge in the domain of hyperspectral image processing. To address this, this paper introduces a novel approach to hyperspectral image classification based on geodesic spatial–spectral collaborative representation. It introduces geodesic distance to extract spectral neighboring information from hyperspectral images and concurrently employs Euclidean distance to extract spatial neighboring information. By integrating collaborative representation with spatial–spectral information, the model is constructed. The collaborative representation coefficients are obtained by solving the model to reconstruct the testing samples, leading to the classification results derived from the minimum reconstruction residuals. Finally, with comparative experiments conducted on three classical hyperspectral image datasets, the effectiveness of the proposed method is substantiated. On the Indian Pines dataset, the proposed algorithm achieved overall accuracy (OA) of 91.33%, average accuracy (AA) of 93.81%, and kappa coefficient (Kappa) of 90.13%. In the case of the Salinas dataset, OA was 95.62%; AA was 97.30%; and Kappa was 93.84%. Lastly, on the PaviaU dataset, OA stood at 95.77%; AA was 94.13%; and Kappa was 94.38%. [ABSTRACT FROM AUTHOR]

Remote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis of current studies becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies. This study undertakes a systematic quantitative literature review to summarize existing literature and discern current research trends in UAV-based applications for plant disease detection and monitoring. Results reveal a global disparity in research on the topic, with Asian countries being the top contributing countries (43 out of 103 papers). World regions such as Oceania and Africa exhibit comparatively lesser representation. To date, research has largely focused on diseases affecting wheat, sugar beet, potato, maize, and grapevine. Multispectral, reg-green-blue, and hyperspectral sensors were most often used to detect and identify disease symptoms, with current trends pointing to approaches integrating multiple sensors and the use of machine learning and deep learning techniques. Future research should prioritize (i) development of cost-effective and user-friendly UAVs, (ii) integration with emerging agricultural technologies, (iii) improved data acquisition and processing efficiency (iv) diverse testing scenarios, and (v) ethical considerations through proper regulations. [ABSTRACT FROM AUTHOR]

Image processing techniques are based nearly exclusively on RGB (red–green–blue) representation, which is significantly influenced by technological issues. The RGB triplet represents a mixture of the wavelength, saturation, and lightness values of light. It leads to unexpected chromaticity artifacts in processing. Therefore, processing based on the wavelength, saturation, and lightness should be more resistant to the introduction of color artifacts. The proposed process of converting RGB values to corresponding wavelengths is not straightforward. In this contribution, a novel simple and accurate method for extracting the wavelength, saturation, and lightness of a color represented by an RGB triplet is described. The conversion relies on the known RGB values of the rainbow spectrum and accommodates variations in color saturation. [ABSTRACT FROM AUTHOR]

Nowadays, hyperspectral imaging (HSI) attracts the interest of many researchers in solving the remote sensing problems especially in various specific domains such as agriculture, snow/ice, object detection and environmental monitoring. In the previous literature, various attempts have been made to extract the critical information through hyperspectral imaging which is not possible through multispectral imaging (MSI). The classification in image processing is one of the important steps to categorize and label the pixels based on some specific rules. There are various supervised and unsupervised approaches which can be used for classification. Since the past decades, various classifiers have been developed and improved to meet the requirement of remote sensing researchers. However, each method has its own merits and demerits and is not applicable in all scenarios. Past literature also concluded that deep learning classifiers are more preferable as compared to machine learning classifiers due to various advantages such as lesser training time for model generation, handle complex data and lesser user intervention requirements. This paper aims to perform the review on various machine learning and deep learning-based classifiers for HSI classification along with challenges and remedial solution of deep learning with hyperspectral imaging. This work also highlights the various limitations of the classifiers which can be resolved with developments and incorporation of well-defined techniques. [ABSTRACT FROM AUTHOR]

Pan-sharpening is a fundamental and crucial task in the remote sensing image processing field, which generates a high-resolution multi-spectral image by fusing a low-resolution multi-spectral image and a high-resolution panchromatic image. Recently, deep learning techniques have shown competitive results in pan-sharpening. However, diverse features in the multi-spectral and panchromatic images are not fully extracted and exploited in existing deep learning methods, which leads to information loss in the pan-sharpening process. To solve this problem, a novel pan-sharpening method based on multi-resolution transformer and two-stage feature fusion is proposed in this article. Specifically, a transformer-based multi-resolution feature extractor is designed to extract diverse image features. Then, to fully exploit features with different content and characteristics, a two-stage feature fusion strategy is adopted. In the first stage, a multi-resolution fusion module is proposed to fuse multi-spectral and panchromatic features at each scale. In the second stage, a shallow-deep fusion module is proposed to fuse shallow and deep features for detail generation. Experiments over QuickBird and WorldView-3 datasets demonstrate that the proposed method outperforms current state-of-the-art approaches visually and quantitatively with fewer parameters. Moreover, the ablation study and feature map analysis also prove the effectiveness of the transformer-based multi-resolution feature extractor and the twostage fusion scheme. [ABSTRACT FROM AUTHOR]

In recent years, hyperspectral image classification techniques have attracted a lot of attention from many scholars because they can be used to model the development of different cities and provide a reference for urban planning and construction. However, due to the difficulty in obtaining hyperspectral images, only a limited number of pixels can be used as training samples. Therefore, how to adequately extract and utilize the spatial and spectral information of hyperspectral images with limited training samples has become a difficult problem. To address this issue, we propose a hyperspectral image classification method based on dense pyramidal convolution and multi-feature fusion (DPCMF). In this approach, two branches are designed to extract spatial and spectral features, respectively. In the spatial branch, dense pyramid convolutions and non-local blocks are used to extract multi-scale local and global spatial features in image samples, which are then fused to obtain spatial features. In the spectral branch, dense pyramidal convolution layers are used to extract spectral features in image samples. Finally, the spatial and spectral features are fused and fed into fully connected layers to obtain classification results. The experimental results show that the overall accuracy (OA) of the method proposed in this paper is 96.74%, 98.10%, 98.92% and 96.67% on the four hyperspectral datasets, respectively. Significant improvements are achieved compared to the five methods of SVM, SSRN, FDSSC, DBMA and DBDA for hyperspectral classification. Therefore, the proposed method can better extract and exploit the spatial and spectral information in image samples when the number of training samples is limited. Provide more realistic and intuitive terrain and environmental conditions for urban planning, design, construction and management. [ABSTRACT FROM AUTHOR]

Learning subtle discriminative feature representation plays a significant role in Fine‐Grained Visual Categorisation (FGVC). The vision transformer (ViT) achieves promising performance in the traditional image classification filed due to its multi‐head self‐attention mechanism. Unfortunately, ViT cannot effectively capture critical feature regions for FGVC due to only focusing on classification token and adopting the strategy of one‐time image input. Besides, the advantage of attention weights fusion is not applied to ViT. To promote the performance of capturing vital regions for FGVC, the authors propose a novel model named RDTrans, which proposes discriminative region with top priority in a recurrent learning way. Specifically, proposed vital regions at each scale will be cropped and amplified as the next input parameters to finally locate the most discriminative region. Furthermore, a distillation learning method is employed to provide better supervision for elevating the generalisation ability. Concurrently, RDTrans can be easily trained end‐to‐end in a weakly‐supervised learning way. Extensive experiments demonstrate that RDTrans yields state‐of‐the‐art performance on four widely used fine‐grained benchmarks, including CUB‐200‐2011, Stanford Cars, Stanford Dogs, and iNat2017. [ABSTRACT FROM AUTHOR]

Image decomposition into its structure and texture components is widely used in various image processing and computer vision applications. It is challenging to extract the structure component from an image having intricate texture since it is difficult to extract the structure from the texture that shares similar color intensity or scale. The aim of this work is to smooth the texture component from the image without affecting the significant image structures and to serve the purpose a structure- aware adaptive joint bilateral texture filtering has been employed. Main contribution in this paper is the designing of the guidance image, used in joint bilateral filtering for texture smoothing. To obtain high efficiency by using the proposed method, authors designed a scale map, which provides the size of the spatial kernel at each pixel using the characteristics of the structure and texture components. The experimental section demonstrates the supremacy of the proposed method over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Desertification is one of the most destructive climate-related issues in the Sudan–Sahel region of Africa. As the assessment of desertification is possible by satellite image analysis using vegetation indices (VIs), this study reports on the technical advantages and capabilities of scripting the 'raster' and 'terra' R-language packages for computing the VIs. The test area which was considered includes the region of the confluence between the Blue and White Niles in Khartoum, southern Sudan, northeast Africa and the Landsat 8–9 OLI/TIRS images taken for the years 2013, 2018 and 2022, which were chosen as test datasets. The VIs used here are robust indicators of plant greenness, and combined with vegetation coverage, are essential parameters for environmental analytics. Five VIs were calculated to compare both the status and dynamics of vegetation through the differences between the images collected within the nine-year span. Using scripts for computing and visualising the VIs over Sudan demonstrates previously unreported patterns of vegetation to reveal climate–vegetation relationships. The ability of the R packages 'raster' and 'terra' to process spatial data was enhanced through scripting to automate image analysis and mapping, and choosing Sudan for the case study enables us to present new perspectives for image processing. [ABSTRACT FROM AUTHOR]

Economical to a fault, coconut oil is a commodity related to fraudulent activities such as oil adulteration for undue profits. Unfortunately, the conventional methods used in the detection of adulteration and toxicants are laborious, destructive, and time-consuming. Hence, it is imperative to engineer a non-destructive and rapid screening test with sufficient accuracy. To that end, the proposed work has an in-house developed imaging system hardware and a method to estimate relevant quality parameters from multispectral imagery. Multispectral images of adulterated coconut oil were analyzed through a cascade of statistical algorithms: Fisher Discriminant Analysis and Bhattacharyya distance respectively. In this work, a functional relationship was developed for the estimation of adulteration level that recorded an R2 of 0.9876 for the training samples and an MSE of 0.0029 for the testing samples. Besides, the proposed imaging system offers flexibility on post-processing of raw measurements as the algorithm is designed to operate from raw multispectral images. In addition, the developed imaging system is economical in its capacity to estimate the adulteration of coconut oil with remarkable accuracy considering the low cost of production. Moreover, the proposed work validates the use of multispectral imagery as an initial screening technique instead of expensive spectroscopy methods. [ABSTRACT FROM AUTHOR]

The large-aperture static imaging spectrometer (LASIS) is an interference spectrometer with high device stability, high throughput, a wide spectral range, and a high spectral resolution. One frame image of the original data cube acquired by the LASIS shows the image superimposed with interference fringes, which is distinctly different from traditional hyperspectral images. For compression studies using this new type of data, a lossless compression scheme that combines a novel data rearrange method and the lossless multispectral and hyperspectral image compression standard CCSDS-123 is presented. In the rearrange approach, the LASIS data cube is rearranged such that the interference information overlapped on the image can be separated, and the results are then processed using the CCSDS-123 standard. Then, several experiments are conducted to investigate the performance of the rearrange method and examine the impact of different CCSDS-123 parameter settings for the LASIS. The experimental results indicate that the proposed scheme provides a 32.9% higher ratio than traditional rearrange methods. Moreover, an adequate parameter combination for this compression scheme for LASIS is presented, and it yields a 19.6% improvement over the default settings suggested by the standard. [ABSTRACT FROM AUTHOR]

Purpose. The purpose of this article is to develop a preprocessing method for digital multispectral remote sensing images obtained through optical and infrared means in the electromagnetic spectrum. The method aims to ensure invariance with respect to positional formation conditions that determine spatial and radiometric resolution. By implementing homomorphic filtering in this method, we can significantly increase the informative value of processed imagery. Methodology. The problem solving, including the development of the spatial and radiometric resolution increase ways for multispectral geospatial data are based on the methods of brightness spatial distribution fusion, methods of data dimension reduction, de-correlation techniques and geometric correction of image spatial distributions. Findings. The method of preprocessing digital remote sensing data has been developed, which is a component of the methodology for identifying geometric shapes (GS) of objects in multi-channel aerospace images, allowing for a significant improvement in their recognition efficiency when noise is present. Originality. The method of preprocessing photogrammetric scenes using homomorphic filtering to enhance their informational significance is proposed. The method ensures invariance to positional conditions of fixation, improves the accuracy of further recognition, eliminates the drawbacks of known methods associated with the existence of parametric uncertainty dependence, the features of fixation of species information, low values of information indices of synthesized images, and computational process peculiarities. Practical value. Practical value is consists in improving of identification accuracy of objects GS in digital geospatial data, in significant increasing of raster multispectral images information value and in rising of automated image processing efficiency. The use of the method can greatly enhance the value and usefulness of multispectral photogrammetric images in a wide range of applications, from environmental monitoring to urban planning. [ABSTRACT FROM AUTHOR]

This article aims to present the methods of the radiometric correction of multispectral images—a short review of the existing techniques. The role of radiometric correction is essential to many applications, especially in precision farming, forestry, and climate analysis. Moreover, this paper presents a new relative approach, which considers the angle of inclination of the terrain and the angle of incidence of electromagnetic radiation on the imaged objects when obtaining the baseline data. This method was developed for data obtained from low altitudes—for imagery data acquired by sensors mounted on UAV platforms. The paper analyses the effect of the correction on the spectral information, i.e., the compatibility of the spectral reflection characteristics obtained from the image with the spectral reflection characteristics obtained in the field. The developed method of correction for multispectral data obtained from low altitudes allows for the mapping of spectral reflection characteristics to an extent that allows for the classification of terrestrial coverage with an accuracy of over 95%. In addition, it is possible to distinguish objects that are very similar in terms of spectral reflection characteristics. This research presents a new method of correction of each spectral channel obtained by the multispectral camera, increasing the accuracy of the results obtained, e.g., based on SAM coefficients or correlations, but also when distinguishing land cover types during classification. The results are characterized by high accuracy (over 94% in classification). [ABSTRACT FROM AUTHOR]

Along with the increase of electricity consumption in society, it means that the quality of power inspection gives higher requirements. The traditional manual inspection method is difficult to ensure the stability of checking quality. Without the popularization of manned distribution stations and smart grid methods, replacing manual inspection with electric power inspection robots can improve the inspection quality and inspection efficiency to a certain extent. Along with theimage processing process which is widely used in the robot system, visual impact is not only an effective way for us to obtain information but also the key reflection of the robot system intelligence. Unmanned aircraft as an extremely important robot has long been widely used in all walks of life. Power inspection is the most common use of unmanned aircraft robot inspection. However, due to extreme criteria such as bad weather, relative motion, and shaking of imaging equipment, inspection images are obtained ambiguously. The quality of the inspection image is related to the timely understanding, analysis, and judgment of the power engineering quality inspection database. In the paper, the unmanned aircraft power inspection robot as a scientific research foothold, according to explore the key technology of anti-internet technology to clear the image ambiguity, proposed a complete disambiguation optimization calculation method. The image denoising optimization calculation method based on the directional characteristics of time-frequency analysis and edge maintenance is selected to carry out the finding of the image, which reasonably solves the image resolution bottleneck problem of GIS in unmanned aircraft robot line inspection. Meanwhile, with the emergence of nanorobots in the 21st century, the application and analysis of remote sensing image processing technology in the field of robotic power inspection in this paper will also bring new vitality and vigor to the research of nanopower inspection robots and solve the problem of unclear images existing in nanorobots. [ABSTRACT FROM AUTHOR]

This article discusses a large number of textural features and integral transformations for the analysis of texture-type images. It also discusses the description and analysis of the features of applying existing methods for segmenting texture areas in images and determining the advantages and disadvantages of these methods and the problems that arise in the segmentation of texture areas in images. The purpose of the ongoing research is to use methods and determine the effectiveness of methods for the analysis of aerospace images, which are a combination of textural regions of natural origin and artificial objects. Currently, the automation of the processing of aerospace information, in particular images of the earth's surface, remains an urgent task. The main goal is to develop models and methods for more efficient use of information technologies for the analysis of multispectral texture-type images in the developed algorithms. The article proposes a comprehensive approach to these issues, that is, the consideration of a large number of textural features by integral transformation to eventually create algorithms and programs applicable to solving a wide class of problems in agriculture. [ABSTRACT FROM AUTHOR]

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Spin‐scan tomographic scanning imagers utilise a reticle with a fixed number of thin slits and nutating optics, and have produced images and video at both infrared and optical wavelengths. However, the fixed nature of these systems reduces their versatility by restricting changes that can be made after manufacture. An alternative rosette‐scan tomographic scanning imager based on similar concepts is proposed to overcome this limitation. A single thin‐slit reticle is rotated to be perpendicular to the line scan angle of each rosette petal, thereby allowing tomographic reconstruction. The parameters of the proposed system can be dynamically changed to achieve different trade‐offs between resolution and frame rate. Initial results obtained from a proof‐of‐concept prototype demonstrate the feasibility of this approach. [ABSTRACT FROM AUTHOR]

There is a need for noninvasive, quantitative methods to characterize wound healing in the context of longitudinal investigations related to regenerative medicine. Such tools have the potential to inform the assessment of wound status and healing progression and aid the development of new treatments. We employed spatial frequency domain imaging (SFDI) to characterize the changes in optical properties of tissue during wound healing progression in a porcine model of split-thickness skin grafts and also in a model of burn wound healing with no graft intervention. Changes in the reduced scattering coefficient measured using SFDI correlated with structural changes reported by histology of biopsies taken concurrently. SFDI was able to measure spatial inhomogeneity in the wounds and predicted heterogeneous healing. In addition, we were able to visualize differences in healing rate, depending on whether a wound was debrided and grafted, versus not debrided and left to heal without intervention apart from topical burn wound care. Changes in the concentration of oxy- and deoxyhemoglobin were also quantified, giving insight into hemodynamic changes during healing.

In recent ages, the use of deep learning approaches to extract ground object information from remote sensing high-resolution images has attracted extensive attention in many fields. Nevertheless, due to the high similarity of features between roads, prevailing deep learning semantic segmentation networks commonly demonstrate reduced continuity in road segmentation. Besides this, the role of advanced computing technologies including cloud and edge infrastructures has also become very important due to large number of images and their storage requirements. In order to better study the road details in images related to remote sensing, this paper suggests a road extraction technique which is basically founded on Dimensional U-Net (DU-Net) network. At the deepening level of the U-Net network, a parallel attention mechanism, known as ProCBAM, is added and implemented to the feature transmission step of the classical U-Net network. Moreover, we use and implement the edge-cloud architecture to develop and construct a unique remote sensing image service system that integrates several datacenters and their related edge infrastructure. In the proposed system, the edge network is primarily used for caching and distributing the processed remote sensing images, while the remote datacenter serves as the cloud platform and is responsible for the storage and processing of original remote sensing images. The results show that the proposed cloud enabled DU-Net model has achieved good performance in road segmentation. We observed that it can achieve improved road segmentation and resolve the issue of reduced continuity of road segmentation when compared with other state-of-the-art learning networks. Moreover, our empirical evaluations suggest that the proposed system not only distributes the workload of processing tasks across the edges but also achieves data efficiency among them, which enhances image processing efficiency and reduces data transmission costs. [ABSTRACT FROM AUTHOR]

• The image fusion methods are comprehensively reviewed, and recent developments of DL are elaborated. • The image fusion applications are briefly discussed. • The imaging technologies are summarized for image fusion. • The spectral and polarized image fusion is broadly conferred. • Future perspectives are comprehensively discussed. Multiple imaging modalities can be combined to provide more information about the real world than a single modality alone. Infrared images discriminate targets with respect to their thermal radiation differences, and visible images are promising for texture details. On the other hand, polarized images deliver intensity and polarization information, and multispectral images dispense the spatial, spectral, and temporal information depending upon the environment. Different sensors provide images with different characteristics, such as type of degradation, important features, textural attributes, etc. Several stimulating tasks have been explored in the last decades based on algorithms, performance assessments, processing techniques, and prospective applications. However, most of the reviews and surveys have not properly addressed the issues of additional possibilities of imaging fusion. The primary goal of this paper is to give a thorough overview of image fusion approaches, including associated background and current breakthroughs. We introduce image fusion and categorize the methods based on conventional image processing, deep learning (DL) architectures, and fusion scenarios. Further, we emphasize the recent DL developments in various image fusion scenarios. However, there are still several difficulties to overcome, including developing more advanced algorithms to support more dependable and real-time practical applications, discussed in future perspectives. This study can assist researchers in coping with multiple imaging modalities, recent fusion developments, and future perspectives. [ABSTRACT FROM AUTHOR]

Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping. [ABSTRACT FROM AUTHOR]

Pan-sharpening is the fusion of panchromatic (PAN) image and multispectral (MS) image through certain rules to generate high-resolution multispectral (HRMS) image with vivid spatial details and uniform spectral distribution. It has become an important technology in remote sensing image processing. Recently, convolutional neural networks based on deep learning have achieved remarkable results in the field of pan-sharpening. Guided by this method, this paper proposes a pan-sharpening network PSAM-NET for remote sensing images based on depth expansion combined with Cross-Attention Fusion. It consists of gradient projection and the main image fusion module. The gradient projection mainly generates the fusion module by stacking low-resolution multispectral images and panchromatic images alternately, and two deep prior regularized optimization problem formulas are respectively solved by the gradient projection algorithm. The other principal image fusion module realizes double branch fusion, which is mainly composed of cross attention fusion and channel attention fusion, to produce an excellent fusion effect. The simulation and real data experiments were performed on the standard datasets WV2, GF-2, and QB. The qualitative analysis and quantitative comparison with the classical pan-sharpening method proved that the spatial information of the image obtained by this method is complete. The spectral distribution is uniform, and the evaluation index also has some advantages. [ABSTRACT FROM AUTHOR]

This research study compares the performance of different digital image processing algorithms based on computer vision segmentation methods to process satellite multispectral images of Normalized Difference Vegetation Index (NDVI) to estimate agricultural cropland yield as a proposal for supporting spatial planning decisions. NDVI multispectral images were collected from Sentinel-2 L2-A satellite with distinctive features to be processed through these algorithms implemented in an owned and friendly software interface developed in MATLAB App Designer. These are based on image color detection, using three techniques: rectangular thresholding method, simple thresholding method, and segmentation through Mahalanobis discriminant classifier. The segmented images were used to estimate cropland yields as a function of NDVI variations and the characteristics of each analyzed image, employing a linear model that assigned a yield to each segmented area as a function of a specific NDVI range. Algorithm accuracy was determined as a function of expected cropland yield. Results show that the rectangular thresholding method tends to average cropland yield value in slightly non-uniform images. In contrast, thresholding by pixel and Mahalanobis methods performed better on highly non-uniform NDVI images, with deviations less than 8% compared with the expected cropland yield. The rectangular thresholding method could be a more straightforward tool regarding computational cost since, e.g., the demarcation of rectangular areas is easier in any cultivated area, facilitating the implementation of spatial support plans for farmers. The proposal is to use the rectangular thresholding method as a planning tool, as the other methods may be used for more accurate estimations. [ABSTRACT FROM AUTHOR]

Due to the limited penetration of visible bands, optical remote sensing images are inevitably contaminated by clouds. Therefore, cloud detection or cloud mask products for optical image processing is a very important step. Compared with conventional optical remote sensing satellites (such as Landsat series and Sentinel-2), sustainable development science Satellite-1 (SDGSAT-1) multi-spectral imager (MII) lacks a short-wave infrared (SWIR) band that can be used to effectively distinguish cloud and snow. To solve the above problems, a cloud detection method based on spectral and gradient features (SGF) for SDGSAT-1 multispectral images is proposed in this paper. According to the differences in spectral features between cloud and other ground objects, the method combines four features, namely, brightness, normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and haze-optimized transformation (HOT) to distinguish cloud and most ground objects. Meanwhile, in order to adapt to different environments, the dynamic threshold using Otsu's method is adopted. In addition, it is worth mentioning that gradient features are used to distinguish cloud and snow in this paper. With the test of SDGSAT-1 multispectral images and comparison experiments, the results show that SGF has excellent performance. The overall accuracy of images with snow surface can reach 90.80%, and the overall accuracy of images with other surfaces is above 94%. [ABSTRACT FROM AUTHOR]

The use of multispectral images has great potential to assess seed quality and represents a significant technological advance in the search for fast and non-destructive analysis techniques. However, the devices currently available are expensive. Thus, this study aimed to propose a low-cost method for acquisition and processing of multispectral images of soybean seeds and to evaluate their potential for rapid determination of seed physiological potential. The study was conducted in three steps: implementation of the multispectral image acquisition system, development of an algorithm for automatic image processing, and evaluation of the relationship between the data obtained through image analysis and the results of standard tests used to evaluate seed physiological potential. A total of 43 variables were assessed, eight related to seed physiological potential (germination and vigor) and 35 obtained from the analysis of the multispectral images. Of the variables obtained from multispectral images, 21 were related to pixel values in the images in the different bands evaluated (green, red, and infrared) and 14 associated with seed morphometric characteristics. The proposed system is efficient in obtaining multispectral images and the algorithm developed was efficient to extract morphometric characteristics and pixel information from the images. The parameters obtained from the NIR spectrum region showed a good relationship with the physiological potential of soybean seeds. [ABSTRACT FROM AUTHOR]