Vehicle detection using aerial thermal infrared images has received significant attention because of its strong capability for day and night observations to supply information for vehicle tracking, traffic monitoring, and road network planning. Compared with aerial visible images, aerial thermal infrared images are not sensitive to lighting conditions. However, they have low contrast and blurred edges. Therefore, a combinational and sparse you-only-look-once (ComS-YOLO) neural network is put forward to accurately and quickly detect vehicles in aerial thermal infrared images. Therein, we adjust the structure of the deep neural network to balance the detection accuracy and running time. In addition, we propose an objective function that utilizes the diagonal distance of the corresponding minimum external rectangle, which prevents non-convergence when there is an inclusion relationship between the prediction and true boxes or in the case of width and height alignment. Furthermore, to avoid over-fitting in the training stage, we eliminate some redundant parameters via constraints and on-line pruning. Finally, experimental results on the NWPU VHR-10 and DARPA VIVID datasets show that the proposed ComS-YOLO network effectively and efficiently identifies the vehicles with a low missed rate and false detection rate. Compared with the Faster R-CNN and a series of YOLO neural networks, the proposed neural network presents satisfactory and competitive results in terms of the detection accuracy and running time. Furthermore, vehicle detection experiments under different environments are also carried out, which shows that our method can achieve an excellent and desired performance on detection accuracy and robustness of vehicle detection.
{"title":"ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images","authors":"Xunxun Zhang, Xiaoyu Lu","doi":"10.1117/1.jrs.18.014508","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014508","url":null,"abstract":"Vehicle detection using aerial thermal infrared images has received significant attention because of its strong capability for day and night observations to supply information for vehicle tracking, traffic monitoring, and road network planning. Compared with aerial visible images, aerial thermal infrared images are not sensitive to lighting conditions. However, they have low contrast and blurred edges. Therefore, a combinational and sparse you-only-look-once (ComS-YOLO) neural network is put forward to accurately and quickly detect vehicles in aerial thermal infrared images. Therein, we adjust the structure of the deep neural network to balance the detection accuracy and running time. In addition, we propose an objective function that utilizes the diagonal distance of the corresponding minimum external rectangle, which prevents non-convergence when there is an inclusion relationship between the prediction and true boxes or in the case of width and height alignment. Furthermore, to avoid over-fitting in the training stage, we eliminate some redundant parameters via constraints and on-line pruning. Finally, experimental results on the NWPU VHR-10 and DARPA VIVID datasets show that the proposed ComS-YOLO network effectively and efficiently identifies the vehicles with a low missed rate and false detection rate. Compared with the Faster R-CNN and a series of YOLO neural networks, the proposed neural network presents satisfactory and competitive results in terms of the detection accuracy and running time. Furthermore, vehicle detection experiments under different environments are also carried out, which shows that our method can achieve an excellent and desired performance on detection accuracy and robustness of vehicle detection.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"285 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139657029","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}
Accurate estimation of forest individual tree characteristics and biomass is very important for monitoring global carbon storage and carbon cycle. In order to solve the problem of calculating individual biomass of various tree species in complex stands, we take terrestrial laser scanning data, unmanned aerial vehicle-laser scanning data, and multispectral data as data sources and extract spectral characteristics, vegetation index characteristics, texture characteristics, and tree height characteristics of diverse forest areas through multispectral classification of tree species. Based on the random forest (RF) algorithm, the extracted features were superimposed and optimized, and the tree species were classified according to the multispectral data combined with field investigation. Then multispectral classification data combined with light detection and ranging (LIDAR) point cloud data were used to classify point cloud species, and then individual tree parameters were extracted for the divided point cloud species, and stand biomass was obtained using the tree biomass calculation model. The results showed that all kinds of tree species could be identified based on RF algorithm by combining multispectral data and LIDAR data. The overall classification accuracy was 66% and the kappa coefficient was 0.59. The recall rate of poplar, cypress, and lacebark-pine was about 75%, except for willow and clove trees, which were blocked by large crown width and caused multiple detection and missed detection. The R2 of diameter at breast height was 0.85, and the root-mean-square error (RMSE) was 5.90 cm. The R2 of the tree height was 0.90, and the RMSE was 1.78 m. Finally, the biomass of each tree species was calculated, and the stand biomass was 66.76 t/hm2, which realized the classification of the whole stand and the measurement of the biomass of each tree. Our study proves that the application of combined multisource remote sensing data to forest biomass estimation has good feasibility.
{"title":"Combining multisource remote sensing data to calculate individual tree biomass in complex stands","authors":"Xugang Lian, Hailang Zhang, Leixue Wang, Yulu Gao, Lifan Shi, Yu Li, Jiang Chang","doi":"10.1117/1.jrs.18.014515","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014515","url":null,"abstract":"Accurate estimation of forest individual tree characteristics and biomass is very important for monitoring global carbon storage and carbon cycle. In order to solve the problem of calculating individual biomass of various tree species in complex stands, we take terrestrial laser scanning data, unmanned aerial vehicle-laser scanning data, and multispectral data as data sources and extract spectral characteristics, vegetation index characteristics, texture characteristics, and tree height characteristics of diverse forest areas through multispectral classification of tree species. Based on the random forest (RF) algorithm, the extracted features were superimposed and optimized, and the tree species were classified according to the multispectral data combined with field investigation. Then multispectral classification data combined with light detection and ranging (LIDAR) point cloud data were used to classify point cloud species, and then individual tree parameters were extracted for the divided point cloud species, and stand biomass was obtained using the tree biomass calculation model. The results showed that all kinds of tree species could be identified based on RF algorithm by combining multispectral data and LIDAR data. The overall classification accuracy was 66% and the kappa coefficient was 0.59. The recall rate of poplar, cypress, and lacebark-pine was about 75%, except for willow and clove trees, which were blocked by large crown width and caused multiple detection and missed detection. The R2 of diameter at breast height was 0.85, and the root-mean-square error (RMSE) was 5.90 cm. The R2 of the tree height was 0.90, and the RMSE was 1.78 m. Finally, the biomass of each tree species was calculated, and the stand biomass was 66.76 t/hm2, which realized the classification of the whole stand and the measurement of the biomass of each tree. Our study proves that the application of combined multisource remote sensing data to forest biomass estimation has good feasibility.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"22 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759716","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}
Junjie Luo, Jiao Guo, Zhe Zhu, Yunlong Du, Yongkai Ye
Accurate orchard spatial distribution information can help government departments to formulate scientific and reasonable agricultural economic policies. However, it is prominent to apply remote sensing images to obtain orchard planting structure information. The traditional multidimensional remote sensing data processing, dimension reduction and classification, which are two separate steps, cannot guarantee that final classification results can be benefited from dimension reduction process. Consequently, to make connection between dimension reduction and classification, this work proposes two neural networks that fuse stack autoencoder and convolutional neural network (CNN) at one-dimension and three-dimension, namely one-dimension and three-dimension fusion stacked autoencoder (FSA) and CNN networks (1D-FSA-CNN and 3D-FSA-CNN). In both networks, the front-end uses a stacked autoencoder (SAE) for dimension reduction, and the back-end uses a CNN with a Softmax classifier for classification. In the experiments, based on Google Earth Engine platform, two groups of orchard datasets are constructed using multi-source remote sensing data (i.e., GaoFen-1, Sentinel-2 and GaoFen-1, and GaoFen-3). Meanwhile, DenseNet201, 3D-CNN, 1D-CNN, and SAE are used for conduct two comparative experiments. The experimental results show that the proposed fusion neural networks achieve the state-of-the-art performance, both accuracies of 3D-FSA-CNN and 1D-FSA-CNN are higher than 95%.
{"title":"Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methods","authors":"Junjie Luo, Jiao Guo, Zhe Zhu, Yunlong Du, Yongkai Ye","doi":"10.1117/1.jrs.18.014514","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014514","url":null,"abstract":"Accurate orchard spatial distribution information can help government departments to formulate scientific and reasonable agricultural economic policies. However, it is prominent to apply remote sensing images to obtain orchard planting structure information. The traditional multidimensional remote sensing data processing, dimension reduction and classification, which are two separate steps, cannot guarantee that final classification results can be benefited from dimension reduction process. Consequently, to make connection between dimension reduction and classification, this work proposes two neural networks that fuse stack autoencoder and convolutional neural network (CNN) at one-dimension and three-dimension, namely one-dimension and three-dimension fusion stacked autoencoder (FSA) and CNN networks (1D-FSA-CNN and 3D-FSA-CNN). In both networks, the front-end uses a stacked autoencoder (SAE) for dimension reduction, and the back-end uses a CNN with a Softmax classifier for classification. In the experiments, based on Google Earth Engine platform, two groups of orchard datasets are constructed using multi-source remote sensing data (i.e., GaoFen-1, Sentinel-2 and GaoFen-1, and GaoFen-3). Meanwhile, DenseNet201, 3D-CNN, 1D-CNN, and SAE are used for conduct two comparative experiments. The experimental results show that the proposed fusion neural networks achieve the state-of-the-art performance, both accuracies of 3D-FSA-CNN and 1D-FSA-CNN are higher than 95%.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"26 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759909","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}
Amazonino Lemos de Castro, Miqueias Lima Duarte, Henrique Ewbank, Roberto Wagner Lourenço
This study was based on analysis of Sentinel-1 (SAR) data to estimate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) during the period 2019 to 2020 in a region with a range of different land uses. The methodology adopted involved the construction of four regression models: linear regression (LR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN). These models aimed to determine vegetation indices based on Sentinel-1 backscattering data, which were used as independent variables. As dependent variables, the NDVI and NDWI obtained via Sentinel-2 data were used. The implementation of the models included the application of cross-validation with an analysis of performance metrics to identify the most effective model. The results revealed that, based on the post-hoc test, the SVM model presented the best performance in the estimation of NDVI and NDWI, with mean R2 values of 0.74 and 0.70, respectively. It is relevant to note that the backscattering coefficient of the vertical-vertical (VV) and vertical-horizontal (VH) polarizations emerged as the variable with the greatest contribution to the models. This finding reinforces the importance of these parameters in the accuracy of estimates. Ultimately, this approach is promising for the creation of time series of NDVI and NDWI in regions that are frequently affected by cloud cover, thus representing a valuable complement to optical sensor data. This integration is particularly valuable for monitoring agricultural crops.
{"title":"Use of synthetic aperture radar data for the determination of normalized difference vegetation index and normalized difference water index","authors":"Amazonino Lemos de Castro, Miqueias Lima Duarte, Henrique Ewbank, Roberto Wagner Lourenço","doi":"10.1117/1.jrs.18.014516","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014516","url":null,"abstract":"This study was based on analysis of Sentinel-1 (SAR) data to estimate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) during the period 2019 to 2020 in a region with a range of different land uses. The methodology adopted involved the construction of four regression models: linear regression (LR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN). These models aimed to determine vegetation indices based on Sentinel-1 backscattering data, which were used as independent variables. As dependent variables, the NDVI and NDWI obtained via Sentinel-2 data were used. The implementation of the models included the application of cross-validation with an analysis of performance metrics to identify the most effective model. The results revealed that, based on the post-hoc test, the SVM model presented the best performance in the estimation of NDVI and NDWI, with mean R2 values of 0.74 and 0.70, respectively. It is relevant to note that the backscattering coefficient of the vertical-vertical (VV) and vertical-horizontal (VH) polarizations emerged as the variable with the greatest contribution to the models. This finding reinforces the importance of these parameters in the accuracy of estimates. Ultimately, this approach is promising for the creation of time series of NDVI and NDWI in regions that are frequently affected by cloud cover, thus representing a valuable complement to optical sensor data. This integration is particularly valuable for monitoring agricultural crops.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"101 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759464","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}
José Victor Orlandi Simões, Rogerio Galante Negri, Felipe Nascimento Souza, Tatiana Sussel Gonçalves Mendes, Adriano Bressane
Climate change is a critical concern that has been greatly affected by human activities, resulting in a rise in greenhouse gas emissions. Its effects have far-reaching impacts on both living and non-living components of ecosystems, leading to alarming outcomes such as a surge in the frequency and severity of fires. This paper presents a data-driven framework that unifies time series of remote sensing images, statistical modeling, and unsupervised classification for mapping fire-damaged areas. To validate the proposed methodology, multiple remote sensing images acquired by the Sentinel-1 satellite between August and October 2021 were collected and analyzed in two case studies comprising Brazilian biomes affected by burns. Our results demonstrate that the proposed approach outperforms another method evaluated in terms of precision metrics and visual adherence. Our methodology achieves the highest overall accuracy of 58.15% and the highest F1 score of 0.72, both of which are higher than the other method. These findings suggest that our approach is more effective in detecting burned areas and may have practical applications in other environmental issues such as landslides, flooding, and deforestation.
{"title":"Unsupervised burned areas detection using multitemporal synthetic aperture radar data","authors":"José Victor Orlandi Simões, Rogerio Galante Negri, Felipe Nascimento Souza, Tatiana Sussel Gonçalves Mendes, Adriano Bressane","doi":"10.1117/1.jrs.18.014513","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014513","url":null,"abstract":"Climate change is a critical concern that has been greatly affected by human activities, resulting in a rise in greenhouse gas emissions. Its effects have far-reaching impacts on both living and non-living components of ecosystems, leading to alarming outcomes such as a surge in the frequency and severity of fires. This paper presents a data-driven framework that unifies time series of remote sensing images, statistical modeling, and unsupervised classification for mapping fire-damaged areas. To validate the proposed methodology, multiple remote sensing images acquired by the Sentinel-1 satellite between August and October 2021 were collected and analyzed in two case studies comprising Brazilian biomes affected by burns. Our results demonstrate that the proposed approach outperforms another method evaluated in terms of precision metrics and visual adherence. Our methodology achieves the highest overall accuracy of 58.15% and the highest F1 score of 0.72, both of which are higher than the other method. These findings suggest that our approach is more effective in detecting burned areas and may have practical applications in other environmental issues such as landslides, flooding, and deforestation.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"14 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759593","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}
A total solar eclipse occurred on April 20, 2023, with the umbral shadow touching the Australian continent over the Ningaloo coastal region, near the town of Exmouth, Western Australia. Eclipse totality lasted ∼1 min, reaching totality at ∼03:29 UTC and happened under cloudless skies. Here, we show that the speed of the Moon’s shadow over the land surface can be estimated from 10 min sampling in both the infrared and visible bands of the Himawari-9 geostationary satellite sensor. The cooling of the land surface due to the passage of the Moon’s shadow over the land is investigated, and temperature drops of 7 K to 15 K are found with cooling rates of 2±1.5 mK s−1. By tracking the time of maximum cooling, the speed of the Moon’s shadow was estimated from thermal data to be 2788±21 km h−1 and from the time of minimum reflectance in the visible data to be 2598±181 km h−1, with a notable time dependence. The methodology and analyses are new and the results compare favorably with NASA’s eclipse data computed using Besselian elements.
2023 年 4 月 20 日发生了日全食，本影在西澳大利亚埃克斯茅斯镇附近的宁格鲁沿海地区上空触及澳大利亚大陆。日全食持续了 1 分钟，在世界协调时 03:29 分达到全食，发生在万里无云的天空下。在此，我们展示了通过对向日葵9号地球静止卫星传感器的红外波段和可见光波段进行10分钟取样，可以估算出月影掠过陆地表面的速度。研究了月影掠过陆地导致的陆地表面降温，发现温度下降了 7 K 至 15 K，降温速率为 2±1.5 mK s-1。通过跟踪最大降温时间，从热数据估算出月影的速度为 2788±21 km h-1，从可见光数据中的最小反射率时间估算出月影的速度为 2598±181 km h-1，两者具有显著的时间依赖性。该方法和分析都是全新的，其结果与美国国家航空航天局使用贝塞尔元素计算的月食数据相比效果更佳。
{"title":"Ningaloo eclipse: moon shadow speed and land surface temperature effects from Himawari-9 satellite measurements","authors":"Fred Prata","doi":"10.1117/1.jrs.18.014511","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014511","url":null,"abstract":"A total solar eclipse occurred on April 20, 2023, with the umbral shadow touching the Australian continent over the Ningaloo coastal region, near the town of Exmouth, Western Australia. Eclipse totality lasted ∼1 min, reaching totality at ∼03:29 UTC and happened under cloudless skies. Here, we show that the speed of the Moon’s shadow over the land surface can be estimated from 10 min sampling in both the infrared and visible bands of the Himawari-9 geostationary satellite sensor. The cooling of the land surface due to the passage of the Moon’s shadow over the land is investigated, and temperature drops of 7 K to 15 K are found with cooling rates of 2±1.5 mK s−1. By tracking the time of maximum cooling, the speed of the Moon’s shadow was estimated from thermal data to be 2788±21 km h−1 and from the time of minimum reflectance in the visible data to be 2598±181 km h−1, with a notable time dependence. The methodology and analyses are new and the results compare favorably with NASA’s eclipse data computed using Besselian elements.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"14 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759594","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}
By leveraging the characteristics of different optical sensors, infrared and visible image fusion generates a fused image that combines prominent thermal radiation targets with clear texture details. Existing methods often focus on a single modality or treat two modalities equally, which overlook the distinctive characteristics of each modality and fail to fully utilize their complementary information. To address this problem, we propose an end-to-end infrared and visible image fusion model based on shared-individual multi-scale feature decomposition. First, to extract multi-scale features from source images, a symmetric multi-scale decomposition encoder consisting of nest connections and a multi-scale receptive field network is designed to capture small, medium, and large-scale features. Second, to sufficiently utilize complementary information, common edge feature maps are introduced to the feature decomposition loss function to decompose extracted features into shared and individual features. Third, to aggregate shared and individual features, a shared-individual self-augmented decoder is proposed to take the individual fusion feature maps as the main input and the shared fusion feature maps as the residual input to assist the decoding process and the reconstruct the fused image. Finally, through comparing subjective evaluations and objective metrics, our method demonstrates its superiority compared with the state-of-the-art approaches.
{"title":"SMFD: an end-to-end infrared and visible image fusion model based on shared-individual multi-scale feature decomposition","authors":"Mingrui Xu, Jun Kong, Min Jiang, Tianshan Liu","doi":"10.1117/1.jrs.18.022203","DOIUrl":"https://doi.org/10.1117/1.jrs.18.022203","url":null,"abstract":"By leveraging the characteristics of different optical sensors, infrared and visible image fusion generates a fused image that combines prominent thermal radiation targets with clear texture details. Existing methods often focus on a single modality or treat two modalities equally, which overlook the distinctive characteristics of each modality and fail to fully utilize their complementary information. To address this problem, we propose an end-to-end infrared and visible image fusion model based on shared-individual multi-scale feature decomposition. First, to extract multi-scale features from source images, a symmetric multi-scale decomposition encoder consisting of nest connections and a multi-scale receptive field network is designed to capture small, medium, and large-scale features. Second, to sufficiently utilize complementary information, common edge feature maps are introduced to the feature decomposition loss function to decompose extracted features into shared and individual features. Third, to aggregate shared and individual features, a shared-individual self-augmented decoder is proposed to take the individual fusion feature maps as the main input and the shared fusion feature maps as the residual input to assist the decoding process and the reconstruct the fused image. Finally, through comparing subjective evaluations and objective metrics, our method demonstrates its superiority compared with the state-of-the-art approaches.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"67 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956174","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 change detection in built-up areas within very high resolution synthetic aperture radar images is a very challenging task due to speckle noise and geometric distortions caused by the unique imaging mechanism. To tackle this issue, we propose an object-based coarse-to-fine change detection method that integrates segmentation and uncertainty analysis techniques. First, we propose a multi-temporal joint multi-scale segmentation method for generating multi-scale segmentation masks with hierarchical nested relationships. Second, we use the neighborhood ratio detector and Jensen–Shannon distance to produce both pixel-level and object-level change maps, respectively. These maps are fused using the Demeter–Shafer evidence theory, resulting in an initial change map. We then apply a threshold to classify parcels within the initial change map into three categories: changed, unchanged, and uncertain. Third, we perform uncertainty analysis and implement progressive classification by support vector machine for uncertain parcels, moving from coarse to fine segmentation levels. Finally, we integrate change maps across all scales to obtain the final change map. The proposed method is evaluated on three datasets from the GF-3 and ICEYE-X6 satellites. The results show that our approach outperforms alternative methods in extracting more comprehensive changed regions.
{"title":"Segmentation-based VHR SAR images built-up area change detection: a coarse-to-fine approach","authors":"Jingxing Zhu, Feng Wang, Hongjian You","doi":"10.1117/1.jrs.18.016503","DOIUrl":"https://doi.org/10.1117/1.jrs.18.016503","url":null,"abstract":"The change detection in built-up areas within very high resolution synthetic aperture radar images is a very challenging task due to speckle noise and geometric distortions caused by the unique imaging mechanism. To tackle this issue, we propose an object-based coarse-to-fine change detection method that integrates segmentation and uncertainty analysis techniques. First, we propose a multi-temporal joint multi-scale segmentation method for generating multi-scale segmentation masks with hierarchical nested relationships. Second, we use the neighborhood ratio detector and Jensen–Shannon distance to produce both pixel-level and object-level change maps, respectively. These maps are fused using the Demeter–Shafer evidence theory, resulting in an initial change map. We then apply a threshold to classify parcels within the initial change map into three categories: changed, unchanged, and uncertain. Third, we perform uncertainty analysis and implement progressive classification by support vector machine for uncertain parcels, moving from coarse to fine segmentation levels. Finally, we integrate change maps across all scales to obtain the final change map. The proposed method is evaluated on three datasets from the GF-3 and ICEYE-X6 satellites. The results show that our approach outperforms alternative methods in extracting more comprehensive changed regions.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"101 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139422836","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}
Ao Li, Yuegong Sun, Cong Feng, Yuan Cheng, Liang Xi
In recent years, graph convolutional networks (GCNs) have attracted increased attention in hyperspectral image (HSI) classification through the utilization of data and their connection graph. However, most existing GCN-based methods have two main drawbacks. First, the constructed graph with pixel-level nodes loses many useful spatial information while high computational cost is required due to large graph size. Second, the joint spatial-spectral structure hidden in HSI are not fully explored for better neighbor correlation preservation, which limits the GCN to achieve promising performance on discriminative feature extraction. To address these problems, we propose a multiscale graph convolutional residual network (MSGCRN) for HSI classification. First, to explore the local spatial–spectral structure, superpixel segmentation is performed on the spectral principal component of HSI at different scales. Thus, the obtained multiscale superpixel areas can capture rich spatial texture division. Second, multiple superpixel-level subgraphs are constructed with adaptive weighted node aggregation, which not only effectively reduces the graph size, but also preserves local neighbor correlation in varying subgraph scales. Finally, a graph convolution residual network is designed for multiscale hierarchical features extraction, which are further integrated into the final discriminative features for HSI classification via a diffusion operation. Moreover, a mini-batch branch is adopted to the large-scale superpixel branch of MSGCRN to further reduce computational cost. Extensive experiments on three public HSI datasets demonstrate the advantages of our MSGCRN model compared to several cutting-edge approaches.
{"title":"Multiscale graph convolution residual network for hyperspectral image classification","authors":"Ao Li, Yuegong Sun, Cong Feng, Yuan Cheng, Liang Xi","doi":"10.1117/1.jrs.18.014504","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014504","url":null,"abstract":"In recent years, graph convolutional networks (GCNs) have attracted increased attention in hyperspectral image (HSI) classification through the utilization of data and their connection graph. However, most existing GCN-based methods have two main drawbacks. First, the constructed graph with pixel-level nodes loses many useful spatial information while high computational cost is required due to large graph size. Second, the joint spatial-spectral structure hidden in HSI are not fully explored for better neighbor correlation preservation, which limits the GCN to achieve promising performance on discriminative feature extraction. To address these problems, we propose a multiscale graph convolutional residual network (MSGCRN) for HSI classification. First, to explore the local spatial–spectral structure, superpixel segmentation is performed on the spectral principal component of HSI at different scales. Thus, the obtained multiscale superpixel areas can capture rich spatial texture division. Second, multiple superpixel-level subgraphs are constructed with adaptive weighted node aggregation, which not only effectively reduces the graph size, but also preserves local neighbor correlation in varying subgraph scales. Finally, a graph convolution residual network is designed for multiscale hierarchical features extraction, which are further integrated into the final discriminative features for HSI classification via a diffusion operation. Moreover, a mini-batch branch is adopted to the large-scale superpixel branch of MSGCRN to further reduce computational cost. Extensive experiments on three public HSI datasets demonstrate the advantages of our MSGCRN model compared to several cutting-edge approaches.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"7 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139495301","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}
Although deep learning-based methods have made remarkable achievements in polarimetric synthetic aperture radar (PolSAR) image classification, these methods require a large number of labeled samples. However, for PolSAR image classification, it is difficult to obtain a large number of labeled samples, which requires extensive human labor and material resources. Therefore, a new PolSAR image classification method based on multi-scale contrastive learning is proposed, which can achieve good classification results with only a small number of labeled samples. During the pre-training process, we propose a multi-scale contrastive learning network model that uses the characteristics of the data itself to train the network by contrastive training. In addition, to capture richer feature information, a multi-scale network structure is introduced. In the training process, considering the diversity and complexity of PolSAR images, we design a hybrid loss function combining the supervised and unsupervised information to achieve better classification performance with limited labeled samples. The experimental results on three real PolSAR datasets have demonstrated that the proposed method outperforms other comparison methods, even with limited labeled samples.
{"title":"Multi-scale contrastive learning method for PolSAR image classification","authors":"Wenqiang Hua, Chen Wang, Nan Sun, Lin Liu","doi":"10.1117/1.jrs.18.014502","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014502","url":null,"abstract":"Although deep learning-based methods have made remarkable achievements in polarimetric synthetic aperture radar (PolSAR) image classification, these methods require a large number of labeled samples. However, for PolSAR image classification, it is difficult to obtain a large number of labeled samples, which requires extensive human labor and material resources. Therefore, a new PolSAR image classification method based on multi-scale contrastive learning is proposed, which can achieve good classification results with only a small number of labeled samples. During the pre-training process, we propose a multi-scale contrastive learning network model that uses the characteristics of the data itself to train the network by contrastive training. In addition, to capture richer feature information, a multi-scale network structure is introduced. In the training process, considering the diversity and complexity of PolSAR images, we design a hybrid loss function combining the supervised and unsupervised information to achieve better classification performance with limited labeled samples. The experimental results on three real PolSAR datasets have demonstrated that the proposed method outperforms other comparison methods, even with limited labeled samples.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"129 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139092110","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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