{"title":"Tunneling- and dewatering-induced rapid differential ground rebound and delayed subsidence measured by InSAR in an urban environment","authors":"K. Wnuk, Wendy Zhou, Marte Gutierrez","doi":"10.1117/1.jrs.18.024512","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024512","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141347077","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":"Benthic river algae mapping using hyperspectral imagery from unoccupied aerial vehicles","authors":"Riley D. Logan, Joseph A. Shaw","doi":"10.1117/1.jrs.18.024513","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024513","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141347207","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}
Soung Sub Lee, Jong Pil Kim, Eungnoh You, Jae-Hyuk Youn, Ho-Hyun Shin
{"title":"Satellite constellation method to achieve desired revisit performance for multiple targets","authors":"Soung Sub Lee, Jong Pil Kim, Eungnoh You, Jae-Hyuk Youn, Ho-Hyun Shin","doi":"10.1117/1.jrs.18.024509","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024509","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141115430","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":"Analyzing building damage from the Kumamoto earthquake using Sentinel-1 data: impact of different acquisition conditions","authors":"T. Nonaka, T. Asaka","doi":"10.1117/1.jrs.18.024508","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024508","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963625","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":"Multiscale region fusion algorithm for 3D plane segmentation","authors":"Qinghua Yang, Tuo Yao, Changfa Wang","doi":"10.1117/1.jrs.18.026503","DOIUrl":"https://doi.org/10.1117/1.jrs.18.026503","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966361","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":"Analyzing land surface temperature changes in the Danjiang River Basin: a MODIS-based reconstruction and assessment before and after the middle route of the South-to-North Water Diversion Project","authors":"Jianhua Guo, Shidong Wang, Jinyan Peng","doi":"10.1117/1.jrs.18.024507","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024507","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980669","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}
Jingfeng Zhang, Bin Zhou, Jin Lu, Ben Wang, Zhipeng Ding, Songyue He
The rapid, accurate, and intelligent extraction of vegetation areas is of great significance for conducting research on forest resource inventory, climate change, and the greenhouse effect. Currently, existing semantic segmentation models suffer from limitations such as insufficient extraction accuracy (ACC) and unbalanced positive and negative categories in datasets. Therefore, we propose the Attention U-Net model for vegetation extraction from Landsat8 operational land imager remote sensing images. By combining the convolutional block attention module, Visual Geometry Group 16 backbone network, and Dice loss, the model alleviates the phenomenon of omission and misclassification of the fragmented vegetation areas and the imbalance of positive and negative classes. In addition, to test the influence of remote sensing images with different band combinations on the ACC of vegetation extraction, we introduce near-infrared (NIR) and short-wave infrared (SWIR) spectral information to conduct band combination operations, thus forming three datasets, namely, the 432 dataset (R, G, B), 543 dataset (NIR, R, G), and 654 dataset (SWIR, NIR, R). In addition, to validate the effectiveness of the proposed model, it was compared with three classic semantic segmentation models, namely, PSP-Net, DeepLabv3+, and U-Net. Experimental results demonstrate that all models exhibit improved extraction performance on false color datasets compared with the true color dataset, particularly on the 654 dataset where vegetation extraction performance is optimal. Moreover, the proposed Attention U-Net achieves the highest overall ACC with mean intersection over union, mean pixel ACC, and ACC reaching 0.877, 0.940, and 0.946, respectively, providing substantial evidence for the effectiveness of the proposed model. Furthermore, the model demonstrates good generalizability and transferability when tested in other regions, indicating its potential for further application and promotion.
{"title":"Vegetation extraction from Landsat8 operational land imager remote sensing imagery based on Attention U-Net and vegetation spectral features","authors":"Jingfeng Zhang, Bin Zhou, Jin Lu, Ben Wang, Zhipeng Ding, Songyue He","doi":"10.1117/1.jrs.18.032403","DOIUrl":"https://doi.org/10.1117/1.jrs.18.032403","url":null,"abstract":"The rapid, accurate, and intelligent extraction of vegetation areas is of great significance for conducting research on forest resource inventory, climate change, and the greenhouse effect. Currently, existing semantic segmentation models suffer from limitations such as insufficient extraction accuracy (ACC) and unbalanced positive and negative categories in datasets. Therefore, we propose the Attention U-Net model for vegetation extraction from Landsat8 operational land imager remote sensing images. By combining the convolutional block attention module, Visual Geometry Group 16 backbone network, and Dice loss, the model alleviates the phenomenon of omission and misclassification of the fragmented vegetation areas and the imbalance of positive and negative classes. In addition, to test the influence of remote sensing images with different band combinations on the ACC of vegetation extraction, we introduce near-infrared (NIR) and short-wave infrared (SWIR) spectral information to conduct band combination operations, thus forming three datasets, namely, the 432 dataset (R, G, B), 543 dataset (NIR, R, G), and 654 dataset (SWIR, NIR, R). In addition, to validate the effectiveness of the proposed model, it was compared with three classic semantic segmentation models, namely, PSP-Net, DeepLabv3+, and U-Net. Experimental results demonstrate that all models exhibit improved extraction performance on false color datasets compared with the true color dataset, particularly on the 654 dataset where vegetation extraction performance is optimal. Moreover, the proposed Attention U-Net achieves the highest overall ACC with mean intersection over union, mean pixel ACC, and ACC reaching 0.877, 0.940, and 0.946, respectively, providing substantial evidence for the effectiveness of the proposed model. Furthermore, the model demonstrates good generalizability and transferability when tested in other regions, indicating its potential for further application and promotion.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140932748","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}
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Mingyang Song, Chao Wang
Reliable rice yield information is critical for global food security. Optical vegetation indices (OVIs) are important parameters for rice yield estimation using remote sensing. Studies have shown that radar vegetation indices (RVIs) are correlated with OVIs. However, research on the implementation of RVIs in rice yield prediction is still in its early stages. In addition, existing deep learning yield prediction models ignore the contribution of temporal features at each time step to the predicted yield and lack the extraction of higher-level features. To address the above issues, this study proposed a rice yield prediction workflow using RVIs and a multiscale one-dimensional convolutional long- and short-term memory network (MultiscaleConv1d-LSTM, MC-LSTM). Sentinel-1 vertical emission and horizontal reception of polarization vertical emission and vertical reception of polarization data and county-level rice yield statistics covering Guangdong Province, China, from 2017 to 2021 were used. The experimental results show that the performance of the RVIs is comparable to that of the OVIs. The proposed MC-LSTM model can effectively improve the accuracy of rice yield prediction. For early rice yield prediction based on RVIs, the optimal accuracy of MC-LSTM [coefficient of determination R2 of 0.67, unbiased root mean square error (ubRMSE) of 217.77 kg/ha] was significantly better than that of the LSTM model (R2 of 0.61, ubRMSE of 229.52 kg/ha). For late rice yield prediction based on RVIs, the optimal accuracy of MC-LSTM (R2 of 0.61 and ubRMSE of 456.54 kg/ha) was significantly better than that of the LSTM model (R2 of 0.55 and ubRMSE of 486.76 kg/ha). The above results show that the proposed method has excellent application prospects in crop yield prediction. This work can provide a new feasible scheme for synthetic-aperture radar data to serve agricultural monitoring and improve the efficiency of rice yield monitoring in a large area.
{"title":"Rice yield prediction using radar vegetation indices from Sentinel-1 data and multiscale one-dimensional convolutional long- and short-term memory network model","authors":"Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Mingyang Song, Chao Wang","doi":"10.1117/1.jrs.18.024505","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024505","url":null,"abstract":"Reliable rice yield information is critical for global food security. Optical vegetation indices (OVIs) are important parameters for rice yield estimation using remote sensing. Studies have shown that radar vegetation indices (RVIs) are correlated with OVIs. However, research on the implementation of RVIs in rice yield prediction is still in its early stages. In addition, existing deep learning yield prediction models ignore the contribution of temporal features at each time step to the predicted yield and lack the extraction of higher-level features. To address the above issues, this study proposed a rice yield prediction workflow using RVIs and a multiscale one-dimensional convolutional long- and short-term memory network (MultiscaleConv1d-LSTM, MC-LSTM). Sentinel-1 vertical emission and horizontal reception of polarization vertical emission and vertical reception of polarization data and county-level rice yield statistics covering Guangdong Province, China, from 2017 to 2021 were used. The experimental results show that the performance of the RVIs is comparable to that of the OVIs. The proposed MC-LSTM model can effectively improve the accuracy of rice yield prediction. For early rice yield prediction based on RVIs, the optimal accuracy of MC-LSTM [coefficient of determination R2 of 0.67, unbiased root mean square error (ubRMSE) of 217.77 kg/ha] was significantly better than that of the LSTM model (R2 of 0.61, ubRMSE of 229.52 kg/ha). For late rice yield prediction based on RVIs, the optimal accuracy of MC-LSTM (R2 of 0.61 and ubRMSE of 456.54 kg/ha) was significantly better than that of the LSTM model (R2 of 0.55 and ubRMSE of 486.76 kg/ha). The above results show that the proposed method has excellent application prospects in crop yield prediction. This work can provide a new feasible scheme for synthetic-aperture radar data to serve agricultural monitoring and improve the efficiency of rice yield monitoring in a large area.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140932731","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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