Surface cover diversity and the complexity of geological structures can seriously impact the accuracy of mineral mapping. To address this issue, we propose a method for lithological classification and analysis based on random forest (RF) and multiple features. Feature vectors, including spectral, polarization, texture, and terrain features, are constructed to provide multidimensional information. Subsequently, these feature vectors are screened based on their discriminative properties for different lithologies to reduce feature redundancy. Finally, the results of lithological classification can be obtained using the RF algorithm based on the selected features. In the experiments conducted in the Qulong copper deposit area, data from Sentinel-1A, Sentinel-2A, and Terra satellites were used to extract multidimensional features. After calculating the Bhattacharyya distance and analyzing the probability density distribution, 17 features selected were input into the RF classifier, achieving an accuracy of 88.83% in lithological classification. This represents a 7.5% improvement compared to exclusively relying on spectral features, and suggests that the proposed method of combining spectral, polarization, texture, and terrain features provides new possibilities for improving the accuracy of field lithological classification.
{"title":"Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China","authors":"Liangyu Chen, Wei Li","doi":"10.1117/1.jrs.17.044504","DOIUrl":"https://doi.org/10.1117/1.jrs.17.044504","url":null,"abstract":"Surface cover diversity and the complexity of geological structures can seriously impact the accuracy of mineral mapping. To address this issue, we propose a method for lithological classification and analysis based on random forest (RF) and multiple features. Feature vectors, including spectral, polarization, texture, and terrain features, are constructed to provide multidimensional information. Subsequently, these feature vectors are screened based on their discriminative properties for different lithologies to reduce feature redundancy. Finally, the results of lithological classification can be obtained using the RF algorithm based on the selected features. In the experiments conducted in the Qulong copper deposit area, data from Sentinel-1A, Sentinel-2A, and Terra satellites were used to extract multidimensional features. After calculating the Bhattacharyya distance and analyzing the probability density distribution, 17 features selected were input into the RF classifier, achieving an accuracy of 88.83% in lithological classification. This represents a 7.5% improvement compared to exclusively relying on spectral features, and suggests that the proposed method of combining spectral, polarization, texture, and terrain features provides new possibilities for improving the accuracy of field lithological classification.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135884241","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}
{"title":"Partial mean and multi-section rank order filtering for order statistic-constant false alarm rate detection in synthetic aperture radar imagery","authors":"Sayed Mahdi Hosseini Miangafsheh, Morteza Kazerooni, Mojtaba Abolghasemi","doi":"10.1117/1.jrs.17.046501","DOIUrl":"https://doi.org/10.1117/1.jrs.17.046501","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209714","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}
Social distancing is a feasible solution to break the chain of the spread of coronavirus disease 2019 (COVID-19). A human crowd detection model was trained with a computational load that can be handled by a companion computer on the unmanned aerial vehicle (UAV) to minimize the spread of COVID-19. The model is designed to be able to measure social distance between people, whether it exceeds predetermined safe limits (1.5 m). The convolutional neural network model was trained using a dataset of 9600 images featuring humans, cyclists, and motorcyclists, with an allocation of 200 images each for testing and hyperparameter tuning. The image dataset was extracted from videos recorded above the UAV in the Institut Teknologi Bandung area, capturing diverse crowd scenarios throughout the day. The pre-trained model for transfer learning method is a single shot detector with MobileNet, ResNet50, and ResNet101 architectures. The measurement of the estimated social distance uses the Euclidian distance with the average Indonesian human as a reference, which is 1.6 m. MobileNet V2 was chosen as a crowd detection model with a lightweight size, which is only 19 MB and the average detection runtime for a single image is only 0.606s, in accordance with the load for the onboard companion computer. MobileNet V2 is also able to detect crowds of people well with the precision value reaching 84.9% and the recall value reaching 87.8%.
{"title":"Convolutional neural network-based crowd detection for COVID-19 social distancing protocol from unmanned aerial vehicles onboard camera","authors":"Leonard Matheus Wastupranata, Rinaldi Munir","doi":"10.1117/1.jrs.17.044502","DOIUrl":"https://doi.org/10.1117/1.jrs.17.044502","url":null,"abstract":"Social distancing is a feasible solution to break the chain of the spread of coronavirus disease 2019 (COVID-19). A human crowd detection model was trained with a computational load that can be handled by a companion computer on the unmanned aerial vehicle (UAV) to minimize the spread of COVID-19. The model is designed to be able to measure social distance between people, whether it exceeds predetermined safe limits (1.5 m). The convolutional neural network model was trained using a dataset of 9600 images featuring humans, cyclists, and motorcyclists, with an allocation of 200 images each for testing and hyperparameter tuning. The image dataset was extracted from videos recorded above the UAV in the Institut Teknologi Bandung area, capturing diverse crowd scenarios throughout the day. The pre-trained model for transfer learning method is a single shot detector with MobileNet, ResNet50, and ResNet101 architectures. The measurement of the estimated social distance uses the Euclidian distance with the average Indonesian human as a reference, which is 1.6 m. MobileNet V2 was chosen as a crowd detection model with a lightweight size, which is only 19 MB and the average detection runtime for a single image is only 0.606s, in accordance with the load for the onboard companion computer. MobileNet V2 is also able to detect crowds of people well with the precision value reaching 84.9% and the recall value reaching 87.8%.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135047410","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}
Matthew Champness, Carlos Ballester Lurbe, Rodrigo Filev Maia, John Hornbuckle
{"title":"Remote sensing to predict soil moisture tension in water saving rice systems of temperate South-Eastern Australia","authors":"Matthew Champness, Carlos Ballester Lurbe, Rodrigo Filev Maia, John Hornbuckle","doi":"10.1117/1.jrs.17.044501","DOIUrl":"https://doi.org/10.1117/1.jrs.17.044501","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135688951","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}
Victoria Stengel, Jessica M. Trevino, Tyler V. King, Scott D. Ducar, Stephen A. Hundt, Konrad C. Hafen, Christopher J. Churchill
Abstract. Aquatic algae and cyanobacteria can impair water-quality and pose risks to human and animal health. Several metrics of in-situ water-quality, including chlorophyll-a, phycocyanin, turbidity, Secchi depth, phytoplankton taxonomy, and hyperspectral reflectance, were collected in coordination with Sentinel-2 satellite overpasses to ascertain water-quality conditions and calibrate satellite detection and estimation of chlorophyll-a concentration. The performance of multiple satellite chlorophyll-a detection indices was evaluated by comparing satellite imagery to field observations of chlorophyll-a concentrations. Seventeen chlorophyll-a spectral indices were implemented using the ACOLITE atmosphere correction; the top performing indices were selected for further evaluation using the Sen2Cor and MAIN atmosphere corrections. The Moses three-band spectral index delivered the strongest linear agreement with field measurements of chlorophyll-a concentration across all reservoir sampling sites (R2 = 0.70). Compared to open-water sites, the Moses three-band spectral index delivered better linear agreement with chlorophyll-a field measurements at inlet sites where there was a greater abundance of near surface aquatic chlorophyll-a concentrations, and the overall chlorophyll-a hyperspectral reflectance signal was stronger. Chlorophyll-a concentration estimates were implemented in a cloud-computation remote sensing platform designed for regional scale remote sensing analysis to map spatiotemporal patterns of aquatic chlorophyll-a across 10 study reservoirs located primarily in north Texas.
{"title":"Near real-time satellite detection and monitoring of aquatic algae and cyanobacteria: how a combination of chlorophyll-a indices and water-quality sampling was applied to north Texas reservoirs","authors":"Victoria Stengel, Jessica M. Trevino, Tyler V. King, Scott D. Ducar, Stephen A. Hundt, Konrad C. Hafen, Christopher J. Churchill","doi":"10.1117/1.JRS.17.044514","DOIUrl":"https://doi.org/10.1117/1.JRS.17.044514","url":null,"abstract":"Abstract. Aquatic algae and cyanobacteria can impair water-quality and pose risks to human and animal health. Several metrics of in-situ water-quality, including chlorophyll-a, phycocyanin, turbidity, Secchi depth, phytoplankton taxonomy, and hyperspectral reflectance, were collected in coordination with Sentinel-2 satellite overpasses to ascertain water-quality conditions and calibrate satellite detection and estimation of chlorophyll-a concentration. The performance of multiple satellite chlorophyll-a detection indices was evaluated by comparing satellite imagery to field observations of chlorophyll-a concentrations. Seventeen chlorophyll-a spectral indices were implemented using the ACOLITE atmosphere correction; the top performing indices were selected for further evaluation using the Sen2Cor and MAIN atmosphere corrections. The Moses three-band spectral index delivered the strongest linear agreement with field measurements of chlorophyll-a concentration across all reservoir sampling sites (R2 = 0.70). Compared to open-water sites, the Moses three-band spectral index delivered better linear agreement with chlorophyll-a field measurements at inlet sites where there was a greater abundance of near surface aquatic chlorophyll-a concentrations, and the overall chlorophyll-a hyperspectral reflectance signal was stronger. Chlorophyll-a concentration estimates were implemented in a cloud-computation remote sensing platform designed for regional scale remote sensing analysis to map spatiotemporal patterns of aquatic chlorophyll-a across 10 study reservoirs located primarily in north Texas.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"37 1","pages":"044514 - 044514"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139330543","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}
Abstract. In order to solve the problems of redundant data acquisition and sparse ground points in dense vegetation areas by conventional unmanned aerial vehicle (UAV) path planning methods, an UAV-airborne LiDAR route optimization method for dense vegetation areas is proposed. First, based on the high-resolution true color remote sensing images of the study area, the “fuzzy” calculation of vegetation coverage for route planning is completed. Then, an optimized ant colony algorithm is proposed for route planning, which introduces vegetation coverage as a reference for route planning and optimizes the pheromone initialization, state transfer rules, pheromone calculation, and update strategies in the classical ant colony algorithm to obtain more ground points. The experimental results show that this method can take into account the vegetation coverage of the flight area and find the area with low vegetation coverage to complete the route planning and efficiently use the sweeping principle of three-dimensional laser scanning to improve the probability of ground point acquisition, with faster iteration speed than the classical ant colony algorithm, and improve the efficiency of ground point acquisition in dense vegetation areas.
{"title":"Optimal ant colony algorithm for UAV airborne LiDAR route planning in densely vegetated areas","authors":"Feifei Tang, Kunyang Li, Feng Xu, Ling Han, Huan Zhang, Zhixing Yang","doi":"10.1117/1.JRS.17.046506","DOIUrl":"https://doi.org/10.1117/1.JRS.17.046506","url":null,"abstract":"Abstract. In order to solve the problems of redundant data acquisition and sparse ground points in dense vegetation areas by conventional unmanned aerial vehicle (UAV) path planning methods, an UAV-airborne LiDAR route optimization method for dense vegetation areas is proposed. First, based on the high-resolution true color remote sensing images of the study area, the “fuzzy” calculation of vegetation coverage for route planning is completed. Then, an optimized ant colony algorithm is proposed for route planning, which introduces vegetation coverage as a reference for route planning and optimizes the pheromone initialization, state transfer rules, pheromone calculation, and update strategies in the classical ant colony algorithm to obtain more ground points. The experimental results show that this method can take into account the vegetation coverage of the flight area and find the area with low vegetation coverage to complete the route planning and efficiently use the sweeping principle of three-dimensional laser scanning to improve the probability of ground point acquisition, with faster iteration speed than the classical ant colony algorithm, and improve the efficiency of ground point acquisition in dense vegetation areas.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"476 1","pages":"046506 - 046506"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139330619","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}
Abstract. Soil maps are essential sources for a diverse range of agricultural and environmental studies; hence, the detection of soil properties using remote sensing technology is a hot topic. Satellites carrying hyperspectral sensors provide possibilities for the estimation of soil properties. But, the main obstacle in soil classification with remote sensing methods is the vegetation that has a spectral signature that mixes with that of the soil. The objective of this study is to detect soil texture properties after eliminating the effects of vegetation using hyperspectral imaging data and reducing the noise by fusion. First, the endmembers common to all images and their abundances are determined. Then the endmembers are classified as stable ones (soil, rock, etc.) and unstable ones (green vegetation, dry vegetation, etc.). This method eliminates vegetation from the images with orthogonal subspace projection (OSP) and fuses multiple images with the weighted mean for a better signal-to-noise-ratio. Finally, the fused image is classified to obtain the soil maps. The method is tested on synthetic images and hyperion hyperspectral images of an area in Texas, United States. With three synthetic images, the individual classification results are 89.14%, 89.81%, and 93.79%. After OSP, the rates increase to 92.23%, 93.13%, and 95.38%, respectively, whereas it increases to 96.97% with fusion. With real images from the dates 22/06/2013, 25/09/2013, and 24/10/2013, the classification accuracies increase from 70.51%, 68.87%, and 63.18% to 71.96%, 71.78%, and 64.17%, respectively. Fusion provides a better improvement in classification with a 75.27% accuracy. The results for the analysis of the real images from 2016 yield similar improvements. The classification accuracies increase from 57.07%, 62.81%, and 63.80% to 58.99%, 63.93%, and 66.33%, respectively. Fusion also provides a better classification accuracy of 69.02% for this experiment. The results show that the method can improve the classification accuracy with the elimination of vegetation and with the fusion of multiple images. The approach is promising and can be applied to various other classification tasks.
{"title":"Soil classification with multi-temporal hyperspectral imagery using spectral unmixing and fusion","authors":"Eylem Kaba, U. Leloglu","doi":"10.1117/1.JRS.17.044513","DOIUrl":"https://doi.org/10.1117/1.JRS.17.044513","url":null,"abstract":"Abstract. Soil maps are essential sources for a diverse range of agricultural and environmental studies; hence, the detection of soil properties using remote sensing technology is a hot topic. Satellites carrying hyperspectral sensors provide possibilities for the estimation of soil properties. But, the main obstacle in soil classification with remote sensing methods is the vegetation that has a spectral signature that mixes with that of the soil. The objective of this study is to detect soil texture properties after eliminating the effects of vegetation using hyperspectral imaging data and reducing the noise by fusion. First, the endmembers common to all images and their abundances are determined. Then the endmembers are classified as stable ones (soil, rock, etc.) and unstable ones (green vegetation, dry vegetation, etc.). This method eliminates vegetation from the images with orthogonal subspace projection (OSP) and fuses multiple images with the weighted mean for a better signal-to-noise-ratio. Finally, the fused image is classified to obtain the soil maps. The method is tested on synthetic images and hyperion hyperspectral images of an area in Texas, United States. With three synthetic images, the individual classification results are 89.14%, 89.81%, and 93.79%. After OSP, the rates increase to 92.23%, 93.13%, and 95.38%, respectively, whereas it increases to 96.97% with fusion. With real images from the dates 22/06/2013, 25/09/2013, and 24/10/2013, the classification accuracies increase from 70.51%, 68.87%, and 63.18% to 71.96%, 71.78%, and 64.17%, respectively. Fusion provides a better improvement in classification with a 75.27% accuracy. The results for the analysis of the real images from 2016 yield similar improvements. The classification accuracies increase from 57.07%, 62.81%, and 63.80% to 58.99%, 63.93%, and 66.33%, respectively. Fusion also provides a better classification accuracy of 69.02% for this experiment. The results show that the method can improve the classification accuracy with the elimination of vegetation and with the fusion of multiple images. The approach is promising and can be applied to various other classification tasks.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"38 1","pages":"044513 - 044513"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139328650","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}
Abstract. Detecting small objects in remote sensing images is a challenging task. Existing object detectors for remote sensing images suffer from two issues: (1) insufficient feature extraction for small objects in the backbone network and (2) feature misalignment and information loss for small objects in the neck network, leading to poor detection performance on small objects. To address these challenges, a small object detector named CABDet for remote sensing images that combines context and attention mechanisms is proposed. Specifically, an enhanced ResNet50 is designed as a novel backbone network that adaptively adjusts the size of receptive fields to fully extract feature information of small objects. Additionally, an adaptive multiscale feature pyramid network (AM-FPN) is proposed. To alleviate the problem of feature misalignment for small objects, AM-FPN leverages self-attention mechanisms to establish semantic and spatial dependencies between adjacent feature layers. Then to mitigate the issue of information loss for small objects, AM-FPN captures semantic dependencies between subregions of current layer features through self-attention mechanisms to preserve channel information. Extensive experiments were conducted on two demanding remote sensing datasets, namely dataset for object detection in aerial images and UCAS-high resolution aerial object detection dataset, to demonstrate the effectiveness of the proposed methodology in achieving superior detection performance when compared with contemporary state-of-the-art approaches.
{"title":"CABDet: context-and-attention-based detector for small object detection in remote sensing images","authors":"Mingzhi Zhang, Xiaohai He, Qizhi Teng, Tong Niu, Honggang Chen","doi":"10.1117/1.JRS.17.044515","DOIUrl":"https://doi.org/10.1117/1.JRS.17.044515","url":null,"abstract":"Abstract. Detecting small objects in remote sensing images is a challenging task. Existing object detectors for remote sensing images suffer from two issues: (1) insufficient feature extraction for small objects in the backbone network and (2) feature misalignment and information loss for small objects in the neck network, leading to poor detection performance on small objects. To address these challenges, a small object detector named CABDet for remote sensing images that combines context and attention mechanisms is proposed. Specifically, an enhanced ResNet50 is designed as a novel backbone network that adaptively adjusts the size of receptive fields to fully extract feature information of small objects. Additionally, an adaptive multiscale feature pyramid network (AM-FPN) is proposed. To alleviate the problem of feature misalignment for small objects, AM-FPN leverages self-attention mechanisms to establish semantic and spatial dependencies between adjacent feature layers. Then to mitigate the issue of information loss for small objects, AM-FPN captures semantic dependencies between subregions of current layer features through self-attention mechanisms to preserve channel information. Extensive experiments were conducted on two demanding remote sensing datasets, namely dataset for object detection in aerial images and UCAS-high resolution aerial object detection dataset, to demonstrate the effectiveness of the proposed methodology in achieving superior detection performance when compared with contemporary state-of-the-art approaches.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"168-169 1","pages":"044515 - 044515"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139330078","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}
Abstract. Remote sensing (RS) scene classification is a highly challenging task because of the unique characteristics of RS scenes, such as high intra-class variability, large inter-class similarity, and various objects with different scales. Attention, interpreted as an important mechanism of the human visual system, can emphasize meaningful features of deep neural networks, which is beneficial for boosting the classification performance. Motivated by it, we present a multi-attention aggregation network (MAANet), which contains various specially designed attention models, for precise RS scene classification. First, a gated attention fluid coding structure is constructed for mining hierarchical gated attention features from RS images. Second, a progressive pyramid refinement architecture is designed to explore correlations of cross-layer attention features to learn enhanced multi-scale representations. Third, a two-stream attention aggregation structure, equipped with three different attention models, is developed to guide the generation of aggregated features. Finally, a scene label prediction module is proposed for scene label prediction. We conduct extensive experiments on three famous RS scene datasets, and the experimental results show that our MAANet outperforms a number of current representative state-of-the-art approaches for the RS scene classification task.
{"title":"Multi-attention aggregation network for remote sensing scene classification","authors":"Xin Wang, Yingying Li, Aiye Shi, Huiyu Zhou","doi":"10.1117/1.JRS.17.046508","DOIUrl":"https://doi.org/10.1117/1.JRS.17.046508","url":null,"abstract":"Abstract. Remote sensing (RS) scene classification is a highly challenging task because of the unique characteristics of RS scenes, such as high intra-class variability, large inter-class similarity, and various objects with different scales. Attention, interpreted as an important mechanism of the human visual system, can emphasize meaningful features of deep neural networks, which is beneficial for boosting the classification performance. Motivated by it, we present a multi-attention aggregation network (MAANet), which contains various specially designed attention models, for precise RS scene classification. First, a gated attention fluid coding structure is constructed for mining hierarchical gated attention features from RS images. Second, a progressive pyramid refinement architecture is designed to explore correlations of cross-layer attention features to learn enhanced multi-scale representations. Third, a two-stream attention aggregation structure, equipped with three different attention models, is developed to guide the generation of aggregated features. Finally, a scene label prediction module is proposed for scene label prediction. We conduct extensive experiments on three famous RS scene datasets, and the experimental results show that our MAANet outperforms a number of current representative state-of-the-art approaches for the RS scene classification task.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"13 1","pages":"046508 - 046508"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139326818","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}
Abstract. Timely monitoring and evaluation of ecological restoration in mining areas is crucial. Based on remote sensing data and deep-learning models, the dynamic changes of bare rock area and vegetation in open-pit mine can be quantitatively monitored and analyzed. Current mining area feature extraction algorithms are limited by single-scale approaches and insufficient information fusion, resulting in low recognition rates. To address this, we proposed an improved Bilateral Segmentation Network (BiSeNetV2) semantic segmentation model (BiSeNetV2 + MSFE + SegHead, BMS), which combines multiscale feature extraction (MSFE) module and segmentation head (SegHead) structures. We utilized BMS model to conduct research on the classification and change monitoring of vegetation areas and mining areas. Our results demonstrated that the accuracy evaluation indicators aAcc, mAcc, and MIoU of the BMS model were better than those of the BiSeNetV2 model, with improvements of 3.5%, 5.5%, and 7.9%, respectively. Meanwhile, compared to the short-term dense concatenate and Twins-PCPVT deep-learning models, the BMS model improved aAcc, mAcc, and MIoU by 3.4%, 8.0%, and 7.3% and 4.4%, 1.1%, and 8.6%, respectively. Accurate and efficient research on ground object classification methods enables quantitative evaluation of mining area environment recovery, providing crucial technical support for ecological monitoring, planning, and governance.
{"title":"Identification of open-pit mines and surrounding vegetation on high-resolution satellite images based on improved bilateral segmentation network semantic segmentation model","authors":"Mian Chen, Bin Yang, Feng Wang, Yan Guo, Tao Duan","doi":"10.1117/1.JRS.17.044518","DOIUrl":"https://doi.org/10.1117/1.JRS.17.044518","url":null,"abstract":"Abstract. Timely monitoring and evaluation of ecological restoration in mining areas is crucial. Based on remote sensing data and deep-learning models, the dynamic changes of bare rock area and vegetation in open-pit mine can be quantitatively monitored and analyzed. Current mining area feature extraction algorithms are limited by single-scale approaches and insufficient information fusion, resulting in low recognition rates. To address this, we proposed an improved Bilateral Segmentation Network (BiSeNetV2) semantic segmentation model (BiSeNetV2 + MSFE + SegHead, BMS), which combines multiscale feature extraction (MSFE) module and segmentation head (SegHead) structures. We utilized BMS model to conduct research on the classification and change monitoring of vegetation areas and mining areas. Our results demonstrated that the accuracy evaluation indicators aAcc, mAcc, and MIoU of the BMS model were better than those of the BiSeNetV2 model, with improvements of 3.5%, 5.5%, and 7.9%, respectively. Meanwhile, compared to the short-term dense concatenate and Twins-PCPVT deep-learning models, the BMS model improved aAcc, mAcc, and MIoU by 3.4%, 8.0%, and 7.3% and 4.4%, 1.1%, and 8.6%, respectively. Accurate and efficient research on ground object classification methods enables quantitative evaluation of mining area environment recovery, providing crucial technical support for ecological monitoring, planning, and governance.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"1 1","pages":"044518 - 044518"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139325825","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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