Guest editors Kaixu Bai, Simone Lolli, and Yuanjian Yang introduce the Special Section on Integrating Remote Sensing, Machine Learning, and Data Science for Air Quality Management.
客座编辑白凯旭、西蒙娜·罗莉和杨元建介绍了“空气质量管理中遥感、机器学习和数据科学的集成”专题。
{"title":"Special Section Guest Editorial: Integrating Remote Sensing, Machine Learning, and Data Science for Air Quality Management","authors":"Kaixu Bai, Simone Lolli, Yuanjian Yang","doi":"10.1117/1.jrs.18.012001","DOIUrl":"https://doi.org/10.1117/1.jrs.18.012001","url":null,"abstract":"Guest editors Kaixu Bai, Simone Lolli, and Yuanjian Yang introduce the Special Section on Integrating Remote Sensing, Machine Learning, and Data Science for Air Quality Management.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"63 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138627854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cheonggil Jin, Minji Kim, Chansol Kim, Yangwon Lee, Kyung-Do Lee, Jae-Hyun Ryu, Chuluong Choi
In precision agriculture, vegetation and soil are monitored by multispectral sensors that can observe outside the visible bands. In contrast to satellites and manned aircraft, unmanned aerial vehicles (UAVs) allow anyone to easily acquire near-real time data at a reasonable price. However, UAV images do not account for the anisotropic reflectance and solar irradiance from the ground surface, so extracting the reflectance of vegetation is difficult. To solve this problem, this study developed a bidirectional reflectance distribution function (BRDF) that expresses the anisotropic reflectance of the Earth’s surface as a function of the geometric relationship with the UAV sensor and the Sun. To compensate for the effect of changes in solar incident energy due to clouds and solar irradiance, the solar irradiance was measured and corrected on the ground rather than in the air to avoid errors due to the flight attitude. Before processing by the BRDF and correcting for the solar irradiance, the UAV obtained striated orthomosaic images for which the vegetation indices were affected by the position and attitude of the Sun and the UAV sensor. After the correction, consistent values were calculated for the vegetation indices throughout the images. The accuracy of the UAV data was analyzed by comparison with Sentinel 2A. Reflectance differences are 0.02% to 6.37% from the image without correction. After applying the correction, it reduced to 0.27%, 0.61%, 0.16%, and 0.65% from the blue, green, red, and near-infrared bands, respectively. This study is valuable for obtaining accurate values for vegetation indices under a wide range of weather and geometric conditions at different sites because UAVs to collect images are a rare case under optimal conditions.
{"title":"Accuracy evaluation of reflectance, normalized difference vegetation index, and normalized difference water index using corrected unmanned aerial vehicle multispectral images by bidirectional reflectance distribution function and solar irradiance","authors":"Cheonggil Jin, Minji Kim, Chansol Kim, Yangwon Lee, Kyung-Do Lee, Jae-Hyun Ryu, Chuluong Choi","doi":"10.1117/1.jrs.17.044512","DOIUrl":"https://doi.org/10.1117/1.jrs.17.044512","url":null,"abstract":"In precision agriculture, vegetation and soil are monitored by multispectral sensors that can observe outside the visible bands. In contrast to satellites and manned aircraft, unmanned aerial vehicles (UAVs) allow anyone to easily acquire near-real time data at a reasonable price. However, UAV images do not account for the anisotropic reflectance and solar irradiance from the ground surface, so extracting the reflectance of vegetation is difficult. To solve this problem, this study developed a bidirectional reflectance distribution function (BRDF) that expresses the anisotropic reflectance of the Earth’s surface as a function of the geometric relationship with the UAV sensor and the Sun. To compensate for the effect of changes in solar incident energy due to clouds and solar irradiance, the solar irradiance was measured and corrected on the ground rather than in the air to avoid errors due to the flight attitude. Before processing by the BRDF and correcting for the solar irradiance, the UAV obtained striated orthomosaic images for which the vegetation indices were affected by the position and attitude of the Sun and the UAV sensor. After the correction, consistent values were calculated for the vegetation indices throughout the images. The accuracy of the UAV data was analyzed by comparison with Sentinel 2A. Reflectance differences are 0.02% to 6.37% from the image without correction. After applying the correction, it reduced to 0.27%, 0.61%, 0.16%, and 0.65% from the blue, green, red, and near-infrared bands, respectively. This study is valuable for obtaining accurate values for vegetation indices under a wide range of weather and geometric conditions at different sites because UAVs to collect images are a rare case under optimal conditions.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"6 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The effect of uranium mining on ground deformation is a relatively unexplored area, especially in terms of surface subsidence related to subsurface ore removal. We use interferometric synthetic aperture radar and spatiotemporal techniques to characterize subsidence signals at the McArthur River underground mine in Canada and the Four Mile in situ leach mine in Australia. We enhance the signal-to-noise ratio of our datasets via time-series techniques and compare results from active periods with results during inactivity to establish a baseline for mining-related signals. We then relate observed surface subsidence to subsurface volumetric strain rates via a voxel parameterization and Bayesian, geostatistical inversion. We use priors on our volumetric strain rates to identify whether these rates are best attributed to ore removal or if additional factors are contributing to subsidence at these sites. We find that the subsidence at McArthur River is best explained by a combination of ore removal and thermal contraction resulting from ground freezing practices. Ore removal via solution extraction alone explains the subsidence at Four Mile, although the localized subsidence pattern and resulting strain rates suggest an intricate combination of sinks and sources in the field, possibly from injection and production well locations and the subsequent flow of solution.
{"title":"Spatio-temporal analysis and volumetric characterization of interferometric synthetic aperture radar-observed deformation signatures related to underground and in situ leach mining","authors":"Elena C. Reinisch, Bradley G. Henderson","doi":"10.1117/1.jrs.17.044511","DOIUrl":"https://doi.org/10.1117/1.jrs.17.044511","url":null,"abstract":"The effect of uranium mining on ground deformation is a relatively unexplored area, especially in terms of surface subsidence related to subsurface ore removal. We use interferometric synthetic aperture radar and spatiotemporal techniques to characterize subsidence signals at the McArthur River underground mine in Canada and the Four Mile in situ leach mine in Australia. We enhance the signal-to-noise ratio of our datasets via time-series techniques and compare results from active periods with results during inactivity to establish a baseline for mining-related signals. We then relate observed surface subsidence to subsurface volumetric strain rates via a voxel parameterization and Bayesian, geostatistical inversion. We use priors on our volumetric strain rates to identify whether these rates are best attributed to ore removal or if additional factors are contributing to subsidence at these sites. We find that the subsidence at McArthur River is best explained by a combination of ore removal and thermal contraction resulting from ground freezing practices. Ore removal via solution extraction alone explains the subsidence at Four Mile, although the localized subsidence pattern and resulting strain rates suggest an intricate combination of sinks and sources in the field, possibly from injection and production well locations and the subsequent flow of solution.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"4 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Priyanka Kumari, Sampriti Soor, Amba Shetty, Shashidhar G. Koolagudi
The compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has significantly advanced our understanding of the mineralogy of Mars. With its enhanced spectral and spatial resolution, CRISM has enabled the identification and characterization of various minerals on the Martian surface, providing valuable insights into Mars’ past climate and geologic history, as well as the evolution of the planet’s atmosphere and climate. We present a comprehensive review of mineral identification on Mars using CRISM data. We discuss the data description, pre-processing techniques, different spectrum libraries, geological characteristics used for mineral identification, challenges, and methodologies used for mineral classification, such as learning models, probabilistic methods, and neural networks. We highlight major findings of minerals on the Martian surface and discuss validation techniques. We conclude with a discussion of further research to address the existing gaps and challenges in this field. Overall, we provide a general understanding of mineral classification using CRISM data and could serve as a helpful resource for researchers and scientists interested in planetary remote sensing and mineral identification on the Martian surface.
{"title":"Mineral classification on Martian surface using CRISM hyperspectral data: a survey","authors":"Priyanka Kumari, Sampriti Soor, Amba Shetty, Shashidhar G. Koolagudi","doi":"10.1117/1.jrs.17.041501","DOIUrl":"https://doi.org/10.1117/1.jrs.17.041501","url":null,"abstract":"The compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has significantly advanced our understanding of the mineralogy of Mars. With its enhanced spectral and spatial resolution, CRISM has enabled the identification and characterization of various minerals on the Martian surface, providing valuable insights into Mars’ past climate and geologic history, as well as the evolution of the planet’s atmosphere and climate. We present a comprehensive review of mineral identification on Mars using CRISM data. We discuss the data description, pre-processing techniques, different spectrum libraries, geological characteristics used for mineral identification, challenges, and methodologies used for mineral classification, such as learning models, probabilistic methods, and neural networks. We highlight major findings of minerals on the Martian surface and discuss validation techniques. We conclude with a discussion of further research to address the existing gaps and challenges in this field. Overall, we provide a general understanding of mineral classification using CRISM data and could serve as a helpful resource for researchers and scientists interested in planetary remote sensing and mineral identification on the Martian surface.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"7 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135042038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Compared with the image captured in the natural scene, the image obtained by unmanned aerial vehicle (UAV) aerial photography has a more complex background and many dense small targets, which puts forward higher requirements for the detection accuracy of the target detection algorithm. However, because the UAV is a kind of small mobile device, how to ensure its real-time detection effect has been a problem. Aiming at these problems, the lightweight YOLOv7 algorithm, namely LRT-YOLOv7, is designed. First, the enhance feature fusion module and the transformer efficient layer aggregation networks module are proposed to improve the performance of feature extraction and fusion to enhance the efficiency of small target detection. Second, aiming at the problems of small target size and complex background in the UAV images, the detection head structure is redesigned in the YOLOv7-tiny algorithm to enhance the multi-scale feature fusion ability of the algorithm and thereby improve the algorithm’s detection accuracy for small targets. Finally, ablation, comparison, and visualization validation experiments were conducted using precision, recall, mean average precision, and frames per second (FPS) as evaluation indicators. The results show that the detection speed of the LRT-YOLOv7 algorithm on the self-made traffic target dataset is 133.8 FPS, and the precision indicator is 84.58%. Therefore, the LRT-YOLOv7 algorithm has high accuracy and real-time performance in traffic target detection tasks for UAV aerial imagery.
{"title":"Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery","authors":"Yang Liu, Ding Ma, Yongfu Wang","doi":"10.1117/1.jrs.17.046505","DOIUrl":"https://doi.org/10.1117/1.jrs.17.046505","url":null,"abstract":"Compared with the image captured in the natural scene, the image obtained by unmanned aerial vehicle (UAV) aerial photography has a more complex background and many dense small targets, which puts forward higher requirements for the detection accuracy of the target detection algorithm. However, because the UAV is a kind of small mobile device, how to ensure its real-time detection effect has been a problem. Aiming at these problems, the lightweight YOLOv7 algorithm, namely LRT-YOLOv7, is designed. First, the enhance feature fusion module and the transformer efficient layer aggregation networks module are proposed to improve the performance of feature extraction and fusion to enhance the efficiency of small target detection. Second, aiming at the problems of small target size and complex background in the UAV images, the detection head structure is redesigned in the YOLOv7-tiny algorithm to enhance the multi-scale feature fusion ability of the algorithm and thereby improve the algorithm’s detection accuracy for small targets. Finally, ablation, comparison, and visualization validation experiments were conducted using precision, recall, mean average precision, and frames per second (FPS) as evaluation indicators. The results show that the detection speed of the LRT-YOLOv7 algorithm on the self-made traffic target dataset is 133.8 FPS, and the precision indicator is 84.58%. Therefore, the LRT-YOLOv7 algorithm has high accuracy and real-time performance in traffic target detection tasks for UAV aerial imagery.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"94 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135092244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Small ship instance segmentation from synthetic aperture radar (SAR) images is a challenging task. Because small ships have smaller scales, indistinct contours, and weak feature response. In addition, background interference and clutter make feature extraction of small ships more difficult. To solve this issue, we propose a scale-aware dimension-wise attention network (SA-DWA-Net) for better small ship instance segmentation in SAR images. SA-DWA-Net has two subnetworks to ensure its desirable instance segmentation of small ships. The first is a scale-aware subnetwork that can fully use low-level location-sensitive information to achieve representative small ship features. The second is a dimension-wise attention subnetwork that can fully utilize high-level semantics-sensitive information for refined small ship feature expression. We perform experiments on two open SSDD and HRSID datasets to verify the effectiveness of the proposed method. Quantitative experimental results show the state-of-the-art SAR ship instance segmentation performance of the proposed SA-DWA-Net. Specifically, SA-DWA-Net surpasses the existing best model by 2.2% box detection average precision (AP) and 5.0% mask segmentation AP on SSDD and by 2.9% box detection AP and 3.7% mask segmentation AP on HRSID. Especially, the small ship mask segmentation AP of the proposed SA-DWA-Net is higher than the existing best model by 4.4% on SSDD and 3.7% on HRSID.
{"title":"Scale-aware dimension-wise attention network for small ship instance segmentation in synthetic aperture radar images","authors":"Xiao Ke, Tianwen Zhang, Zikang Shao","doi":"10.1117/1.jrs.17.046504","DOIUrl":"https://doi.org/10.1117/1.jrs.17.046504","url":null,"abstract":"Small ship instance segmentation from synthetic aperture radar (SAR) images is a challenging task. Because small ships have smaller scales, indistinct contours, and weak feature response. In addition, background interference and clutter make feature extraction of small ships more difficult. To solve this issue, we propose a scale-aware dimension-wise attention network (SA-DWA-Net) for better small ship instance segmentation in SAR images. SA-DWA-Net has two subnetworks to ensure its desirable instance segmentation of small ships. The first is a scale-aware subnetwork that can fully use low-level location-sensitive information to achieve representative small ship features. The second is a dimension-wise attention subnetwork that can fully utilize high-level semantics-sensitive information for refined small ship feature expression. We perform experiments on two open SSDD and HRSID datasets to verify the effectiveness of the proposed method. Quantitative experimental results show the state-of-the-art SAR ship instance segmentation performance of the proposed SA-DWA-Net. Specifically, SA-DWA-Net surpasses the existing best model by 2.2% box detection average precision (AP) and 5.0% mask segmentation AP on SSDD and by 2.9% box detection AP and 3.7% mask segmentation AP on HRSID. Especially, the small ship mask segmentation AP of the proposed SA-DWA-Net is higher than the existing best model by 4.4% on SSDD and 3.7% on HRSID.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":" 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Landslide susceptibility prediction (LSP) is crucial for hazard prevention and geological risk assessment. Support vector machine (SVM) is widely used for LSP, but its parameter optimization problem affects the prediction accuracy and generalization ability of the model, and variations in parameter combinations may result in different prediction outcomes, which brings some challenges to the application of the model. We present a procedure for LSP using the gray wolf optimization (GWO) algorithm to optimize SVM models in the Sanshui District, Foshan City, China. Fifteen factors affecting landslide susceptibility are selected and processed by the natural breakpoint method and normalization method. To prevent overfitting and improve the generalization ability of the model, the five factors with high correlation are excluded using the Pearson correlation coefficient. The grid search method and GWO are used to optimize the SVM parameters and establish the GWO-SVM model. The results indicated that the GWO-SVM model, which incorporated the normalization method (referred to as GWO-SVM-NOR), demonstrated superior predictive accuracy, achieving an impressive area under the curve value of 0.886. The gray wolf algorithm improves the fitting accuracy of SVM and optimizes the model prediction performance with better stability, which is suitable for predicting areas susceptible to landslides.
{"title":"Landslide susceptibility prediction by gray wolf optimized support vector machine model under different factor states","authors":"GongHao Duan, Jie Hu, LiXu Deng, Jie Fu","doi":"10.1117/1.jrs.17.044510","DOIUrl":"https://doi.org/10.1117/1.jrs.17.044510","url":null,"abstract":"Landslide susceptibility prediction (LSP) is crucial for hazard prevention and geological risk assessment. Support vector machine (SVM) is widely used for LSP, but its parameter optimization problem affects the prediction accuracy and generalization ability of the model, and variations in parameter combinations may result in different prediction outcomes, which brings some challenges to the application of the model. We present a procedure for LSP using the gray wolf optimization (GWO) algorithm to optimize SVM models in the Sanshui District, Foshan City, China. Fifteen factors affecting landslide susceptibility are selected and processed by the natural breakpoint method and normalization method. To prevent overfitting and improve the generalization ability of the model, the five factors with high correlation are excluded using the Pearson correlation coefficient. The grid search method and GWO are used to optimize the SVM parameters and establish the GWO-SVM model. The results indicated that the GWO-SVM model, which incorporated the normalization method (referred to as GWO-SVM-NOR), demonstrated superior predictive accuracy, achieving an impressive area under the curve value of 0.886. The gray wolf algorithm improves the fitting accuracy of SVM and optimizes the model prediction performance with better stability, which is suitable for predicting areas susceptible to landslides.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"47 s162","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We provide a speckle aware image segmentation algorithm for synthetic aperture radar (SAR) data. It uses search based segmentation using a three-component machine learning model where speckle noise is considered as discrete component of the feature description. This method allows for the removal of the need for a de-speckling filter during the feature extraction process for SAR images, resulting in a more efficient and accurate approach. A three-component model is used to efficiently represent a feature in SAR data. The algorithm is used to segment different crops from Sentinel-1 C-band SAR data. We describe the search-based segmentation algorithm, three-component model, and its design using K-NN algorithm. We tested the proposed algorithm against K-NN based segmentation on Sentinel-1 images de-speckled using widely used Lee, Refine Lee, Frost, and Gamma-MAP filters. The proposed method is found to produce better classification accuracy compared to results from K-NN and commonly used de-speckling filters.
{"title":"Speckle aware spatial search based segmentation algorithm for crop classification in SAR images using a three component K-NN model","authors":"Chandran Bipin, Chandu Venkateswara Rao, Padavala Veera Sridevi","doi":"10.1117/1.jrs.17.048503","DOIUrl":"https://doi.org/10.1117/1.jrs.17.048503","url":null,"abstract":"We provide a speckle aware image segmentation algorithm for synthetic aperture radar (SAR) data. It uses search based segmentation using a three-component machine learning model where speckle noise is considered as discrete component of the feature description. This method allows for the removal of the need for a de-speckling filter during the feature extraction process for SAR images, resulting in a more efficient and accurate approach. A three-component model is used to efficiently represent a feature in SAR data. The algorithm is used to segment different crops from Sentinel-1 C-band SAR data. We describe the search-based segmentation algorithm, three-component model, and its design using K-NN algorithm. We tested the proposed algorithm against K-NN based segmentation on Sentinel-1 images de-speckled using widely used Lee, Refine Lee, Frost, and Gamma-MAP filters. The proposed method is found to produce better classification accuracy compared to results from K-NN and commonly used de-speckling filters.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"3 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135391316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The advancement of near-ground remote sensing and artificial intelligence techniques has revolutionized field surveys, replacing traditional manual methods. Nevertheless, understanding and exploring the growth patterns and intricate morphology of natural elm tree crowns present significant challenges, especially when attempting to extract their features, which are often susceptible to interference from surrounding grass and vegetation. In addition, existing detection and segmentation models based on convolutional neural networks exhibit redundancies in their network architectures and employ less efficient algorithms, such as mask region-based convolutional neural networks. As a result, these models may not be the most suitable options for analyzing extensive and highly detailed remote-sensing image data. We focus on detecting trees in semi-arid regions and extracting their canopy parameters, such as canopy width and area. A training set is established by outlining a total of 20,594 tree canopies on high-spatial resolution unmanned aerial vehicle images. A two-stage instance segmentation model is proposed to develop a method for individual tree detection and efficient extraction of canopy parameters in complex natural environments. The results demonstrate the method’s capability to accurately detect the location, number, and canopy parameters (e.g., crown width and area) of individual trees in diverse natural scenes. The model achieves a detection speed of 13.3 fps@1024, with the model weight parameters totaling 8.08 M and computation requiring 8.96 Giga floating point operations per seconds (GFLOPs). Moreover, the detection accuracy and segmentation accuracy of individual trees on the validation set are reported as 0.463 and 0.465, respectively. Compared with Mack RCNN and Mask Scoring RCNN, the proposed method reduces the weight parameters and computational complexity of the model by 82.4%, 83.5% and 96.8%, 92.8%, respectively, while increasing the inference speed by 47.4% and 26.3%. This method offers an efficient and accurate solution for obtaining the structural parameters of individual trees.
{"title":"Individual tree crown extraction of natural elm in UAV RGB imagery via an efficient two-stage instance segmentation model","authors":"Bin Yang, Qing Li","doi":"10.1117/1.jrs.17.044509","DOIUrl":"https://doi.org/10.1117/1.jrs.17.044509","url":null,"abstract":"The advancement of near-ground remote sensing and artificial intelligence techniques has revolutionized field surveys, replacing traditional manual methods. Nevertheless, understanding and exploring the growth patterns and intricate morphology of natural elm tree crowns present significant challenges, especially when attempting to extract their features, which are often susceptible to interference from surrounding grass and vegetation. In addition, existing detection and segmentation models based on convolutional neural networks exhibit redundancies in their network architectures and employ less efficient algorithms, such as mask region-based convolutional neural networks. As a result, these models may not be the most suitable options for analyzing extensive and highly detailed remote-sensing image data. We focus on detecting trees in semi-arid regions and extracting their canopy parameters, such as canopy width and area. A training set is established by outlining a total of 20,594 tree canopies on high-spatial resolution unmanned aerial vehicle images. A two-stage instance segmentation model is proposed to develop a method for individual tree detection and efficient extraction of canopy parameters in complex natural environments. The results demonstrate the method’s capability to accurately detect the location, number, and canopy parameters (e.g., crown width and area) of individual trees in diverse natural scenes. The model achieves a detection speed of 13.3 fps@1024, with the model weight parameters totaling 8.08 M and computation requiring 8.96 Giga floating point operations per seconds (GFLOPs). Moreover, the detection accuracy and segmentation accuracy of individual trees on the validation set are reported as 0.463 and 0.465, respectively. Compared with Mack RCNN and Mask Scoring RCNN, the proposed method reduces the weight parameters and computational complexity of the model by 82.4%, 83.5% and 96.8%, 92.8%, respectively, while increasing the inference speed by 47.4% and 26.3%. This method offers an efficient and accurate solution for obtaining the structural parameters of individual trees.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"22 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the field of remote sensing, hyperspectral image (HSI) classification is a widely used technique. Recently, there has been an increasing focus on utilizing superpixels for HSI classification. However, noise pixels in superpixels may lead to unsatisfactory classification results. To address this issue, an effective superpixel sparse representation classification method with multiple features and L0 smoothing is proposed. In this method, multifeature extraction utilizes the diversity of HSIs’ spectral–spatial information, band fusion effectively reduces redundant information and noise of HSIs, and L0 smoothing improves superpixel segmentation results by strengthening homogeneous neighborhoods and edges. Meanwhile, simple linear iterative clustering is adopted to acquire superpixels of HSIs. Finally, the majority voting strategy is adopted to determine the final classification result, improving the classification accuracy. To verify the performance of the proposed method, three hyperspectral datasets are selected for experiments. The experimental results show that the proposed method is superior to some famous classification methods.
{"title":"Effective superpixel sparse representation classification method with multiple features and L0 smoothing for hyperspectral images","authors":"Huixian Lin, Hong Du, Xiaoguang Zhang","doi":"10.1117/1.jrs.17.048502","DOIUrl":"https://doi.org/10.1117/1.jrs.17.048502","url":null,"abstract":"In the field of remote sensing, hyperspectral image (HSI) classification is a widely used technique. Recently, there has been an increasing focus on utilizing superpixels for HSI classification. However, noise pixels in superpixels may lead to unsatisfactory classification results. To address this issue, an effective superpixel sparse representation classification method with multiple features and L0 smoothing is proposed. In this method, multifeature extraction utilizes the diversity of HSIs’ spectral–spatial information, band fusion effectively reduces redundant information and noise of HSIs, and L0 smoothing improves superpixel segmentation results by strengthening homogeneous neighborhoods and edges. Meanwhile, simple linear iterative clustering is adopted to acquire superpixels of HSIs. Finally, the majority voting strategy is adopted to determine the final classification result, improving the classification accuracy. To verify the performance of the proposed method, three hyperspectral datasets are selected for experiments. The experimental results show that the proposed method is superior to some famous classification methods.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"27 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135932788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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