Alaleh Fallah, F. Samadzadegan, Farzaneh Dadras Javan
{"title":"Object-based strategy for generating high-resolution four-dimensional thermal surface models of buildings based on integration of visible and thermal unmanned aerial vehicle imagery","authors":"Alaleh Fallah, F. Samadzadegan, Farzaneh Dadras Javan","doi":"10.1117/1.jrs.18.034504","DOIUrl":"https://doi.org/10.1117/1.jrs.18.034504","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805108","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":"Frequent oversights in on-orbit modulation transfer function estimation of optical imager onboard EO satellites","authors":"Bhaskar Dubey, Anuja Sharma, Shilpa Prakash, Nikunj P. Darji, Debajyoti Dhar","doi":"10.1117/1.jrs.18.036501","DOIUrl":"https://doi.org/10.1117/1.jrs.18.036501","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822612","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":"Comprehensive comparison of different gridded precipitation products over geographic regions of Türkiye","authors":"Behnam Khorrami, O. Sahin, Orhan Gunduz","doi":"10.1117/1.jrs.18.034503","DOIUrl":"https://doi.org/10.1117/1.jrs.18.034503","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655233","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}
Qazi Muhammad Yasir, Zhijie Zhang, Jintong Ren, Guihong Wang, Muhammad Naveed, Zahid Jahangir, Atta-ur- Rahman
Understanding the impact of climate change on Earth presents a significant scientific challenge. Monitoring changes in terrestrial ecosystems, including leaf water content, is essential for assessing plant transpiration, water use efficiency, and physiological processes. Optical remote sensing, utilizing multi-angular reflectance measurements in the near infrared and shortwave infrared wavelengths, offers a precise method for estimating leaf water content. We propose and evaluate a new index based on multi-angular reflection, using 256 leaf samples from 10 plant species for calibration and 683 samples for validation. Hyperspectral indices derived from multi-angular spectra were assessed, facilitating efficient leaf water content analysis with minimal time and specific bands required. We investigate the relationship of leaf water content using spectral indices and apply linear and nonlinear regression models to calibration data, resulting in two indices for each indicator. The newly proposed indices, (R1−R2)/(R1−R3) for linear and (R1905−R1840)/(R1905−R1875) for nonlinear, demonstrate high coefficients of determination for leaf water content (>0.94) using multi-angular reflectance measurements. Published spectral indices exhibit weak relationships with our calibration dataset. The proposed leaf water content indices perform well, with an overall root mean square error of 0.0024 (g/cm2) and 0.0026 (g/cm2) for linear and nonlinear indices, respectively, validated by Leaf Optical Properties Experiment, ANGERS, and multi-angular datasets. The (R1−R2)/(R1−R3) bands show promise for leaf water content estimation. Future studies should encompass more plant species and field data.
{"title":"Spectral index for estimating leaf water content across diverse plant species using multiple viewing angles","authors":"Qazi Muhammad Yasir, Zhijie Zhang, Jintong Ren, Guihong Wang, Muhammad Naveed, Zahid Jahangir, Atta-ur- Rahman","doi":"10.1117/1.jrs.18.042603","DOIUrl":"https://doi.org/10.1117/1.jrs.18.042603","url":null,"abstract":"Understanding the impact of climate change on Earth presents a significant scientific challenge. Monitoring changes in terrestrial ecosystems, including leaf water content, is essential for assessing plant transpiration, water use efficiency, and physiological processes. Optical remote sensing, utilizing multi-angular reflectance measurements in the near infrared and shortwave infrared wavelengths, offers a precise method for estimating leaf water content. We propose and evaluate a new index based on multi-angular reflection, using 256 leaf samples from 10 plant species for calibration and 683 samples for validation. Hyperspectral indices derived from multi-angular spectra were assessed, facilitating efficient leaf water content analysis with minimal time and specific bands required. We investigate the relationship of leaf water content using spectral indices and apply linear and nonlinear regression models to calibration data, resulting in two indices for each indicator. The newly proposed indices, (R1−R2)/(R1−R3) for linear and (R1905−R1840)/(R1905−R1875) for nonlinear, demonstrate high coefficients of determination for leaf water content (>0.94) using multi-angular reflectance measurements. Published spectral indices exhibit weak relationships with our calibration dataset. The proposed leaf water content indices perform well, with an overall root mean square error of 0.0024 (g/cm2) and 0.0026 (g/cm2) for linear and nonlinear indices, respectively, validated by Leaf Optical Properties Experiment, ANGERS, and multi-angular datasets. The (R1−R2)/(R1−R3) bands show promise for leaf water content estimation. Future studies should encompass more plant species and field data.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778098","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}
Xiaohui Liu, Lei Zhang, Rui Wang, Xiaoyu Li, Jiyang Xu, Xiaochen Lu
High-resolution remote sensing images (HRRSIs) contain rich local spatial information and long-distance location dependence, which play an important role in semantic segmentation tasks and have received more and more research attention. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In most networks, there are numerous small-scale object omissions and large-scale object fragmentations in the segmentation results because of insufficient local feature extraction and low global information utilization. A network cascaded by convolution neural network and global–local attention transformer is proposed called CNN-transformer cascade network. First, convolution blocks and global–local attention transformer blocks are used to extract multiscale local features and long-range location information, respectively. Then a multilevel channel attention integration block is designed to fuse geometric features and semantic features of different depths and revise the channel weights through the channel attention module to resist the interference of redundant information. Finally, the smoothness of the segmentation is improved through the implementation of upsampling using a deconvolution operation. We compare our method with several state-of-the-art methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can improve the integrity and independence of multiscale objects segmentation results.
{"title":"Cascaded CNN and global–local attention transformer network-based semantic segmentation for high-resolution remote sensing image","authors":"Xiaohui Liu, Lei Zhang, Rui Wang, Xiaoyu Li, Jiyang Xu, Xiaochen Lu","doi":"10.1117/1.jrs.18.034502","DOIUrl":"https://doi.org/10.1117/1.jrs.18.034502","url":null,"abstract":"High-resolution remote sensing images (HRRSIs) contain rich local spatial information and long-distance location dependence, which play an important role in semantic segmentation tasks and have received more and more research attention. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In most networks, there are numerous small-scale object omissions and large-scale object fragmentations in the segmentation results because of insufficient local feature extraction and low global information utilization. A network cascaded by convolution neural network and global–local attention transformer is proposed called CNN-transformer cascade network. First, convolution blocks and global–local attention transformer blocks are used to extract multiscale local features and long-range location information, respectively. Then a multilevel channel attention integration block is designed to fuse geometric features and semantic features of different depths and revise the channel weights through the channel attention module to resist the interference of redundant information. Finally, the smoothness of the segmentation is improved through the implementation of upsampling using a deconvolution operation. We compare our method with several state-of-the-art methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can improve the integrity and independence of multiscale objects segmentation results.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576223","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}
Jingwei Fan, Donghua Chen, Chen Zou, Qihang Zhen, Yisha Du, Deting Jiang, Saisai Liu, Mei Zan
Soil moisture is a key factor affecting the growth of crops, and microwave remote sensing is one of the most important methods for inverse studies of soil moisture in agricultural fields in recent years. Cotton is a typical water-demanding crop in arid zones, and accurate estimation of soil moisture information in cotton fields is extremely important for optimizing irrigation management, improving water use efficiency, and increasing cotton yield. This study focuses on extracting feature sets by combining Sentinel-1 and Gaofen-6 satellite data, constructing convolutional neural network (CNN), random forest, support vector regression, and eXtreme gradient boosting model to estimate the soil moisture in cotton fields in Shihezi area of Xinjiang, and designing eight groups of experiments according to the different input data sources. The experimental results show that the accuracy of the soil moisture estimate in cotton fields in arid areas with multi-source data is significantly better than that of a single data source. Moreover, the CNN was best estimated when using multi-source data feature sets as inputs, with a coefficient of determination of 0.789, a root mean square error of 0.0249 cm3/cm3, and an average absolute error of 0.0198 cm3/cm3 for its CNN model. This result demonstrates the effectiveness of CNN in soil moisture estimation and also provides a new method for the use of multi-source remote sensing data for accurate soil moisture estimation in cotton fields in arid areas, and also explores the application of Gaofen-6 data in soil moisture.
{"title":"Monitoring soil moisture in cotton fields with synthetic aperture radar and optical data in arid and semi-arid regions","authors":"Jingwei Fan, Donghua Chen, Chen Zou, Qihang Zhen, Yisha Du, Deting Jiang, Saisai Liu, Mei Zan","doi":"10.1117/1.jrs.18.034501","DOIUrl":"https://doi.org/10.1117/1.jrs.18.034501","url":null,"abstract":"Soil moisture is a key factor affecting the growth of crops, and microwave remote sensing is one of the most important methods for inverse studies of soil moisture in agricultural fields in recent years. Cotton is a typical water-demanding crop in arid zones, and accurate estimation of soil moisture information in cotton fields is extremely important for optimizing irrigation management, improving water use efficiency, and increasing cotton yield. This study focuses on extracting feature sets by combining Sentinel-1 and Gaofen-6 satellite data, constructing convolutional neural network (CNN), random forest, support vector regression, and eXtreme gradient boosting model to estimate the soil moisture in cotton fields in Shihezi area of Xinjiang, and designing eight groups of experiments according to the different input data sources. The experimental results show that the accuracy of the soil moisture estimate in cotton fields in arid areas with multi-source data is significantly better than that of a single data source. Moreover, the CNN was best estimated when using multi-source data feature sets as inputs, with a coefficient of determination of 0.789, a root mean square error of 0.0249 cm3/cm3, and an average absolute error of 0.0198 cm3/cm3 for its CNN model. This result demonstrates the effectiveness of CNN in soil moisture estimation and also provides a new method for the use of multi-source remote sensing data for accurate soil moisture estimation in cotton fields in arid areas, and also explores the application of Gaofen-6 data in soil moisture.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522559","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}
Classification of complex and large hyperspectral images (HSIs) with machine learning (ML) algorithms is an important research area. Recently, explainable artificial intelligence (XAI), which helps to explain and interpret black-box ML algorithms, has become popular. Our study aims to present extensive research on the use of XAI methods in explaining the band effect in HSI classification and the impact of reducing the high band number of HSIs by feature selection on the performance of the classifiers. The importance levels of the spectral bands that are effective in the decisions of the different ML classifiers were examined with the deep reinforcement learning and XAI methods, such as Shapley additive explanations (SHAP) and permutation feature importance (PFI). Our work selects representative bands using SHAP and PFI as XAI analysis techniques. We evaluated the XAI-based band selection performance on three publicly available HSI datasets using random forest, light gradient-boosting machine, and extreme gradient boosting classifier algorithms. The results obtained by applying XAI and deep learning methods were used to select spectral bands. Additionally, principal component analysis, a common dimension reduction technique, was performed on the dataset used in our study. Comparable performance evaluation shows that XAI-based methods choose informative bands and outperform other methods in the subsequent tasks. Thus the global and class-based effects of spectral bands can be explained, and the performance of classifiers can be improved by eliminating features that have a negative impact on classification. In HSI classification, studies examining the decisions of ML classifiers using XAI techniques are limited. Our study is one of the pioneer studies in the usage of XAI in HSI classification.
{"title":"Optimal band selection using explainable artificial intelligence for machine learning-based hyperspectral image classification","authors":"Saziye Ozge Atik, Muhammed Enes Atik","doi":"10.1117/1.jrs.18.042604","DOIUrl":"https://doi.org/10.1117/1.jrs.18.042604","url":null,"abstract":"Classification of complex and large hyperspectral images (HSIs) with machine learning (ML) algorithms is an important research area. Recently, explainable artificial intelligence (XAI), which helps to explain and interpret black-box ML algorithms, has become popular. Our study aims to present extensive research on the use of XAI methods in explaining the band effect in HSI classification and the impact of reducing the high band number of HSIs by feature selection on the performance of the classifiers. The importance levels of the spectral bands that are effective in the decisions of the different ML classifiers were examined with the deep reinforcement learning and XAI methods, such as Shapley additive explanations (SHAP) and permutation feature importance (PFI). Our work selects representative bands using SHAP and PFI as XAI analysis techniques. We evaluated the XAI-based band selection performance on three publicly available HSI datasets using random forest, light gradient-boosting machine, and extreme gradient boosting classifier algorithms. The results obtained by applying XAI and deep learning methods were used to select spectral bands. Additionally, principal component analysis, a common dimension reduction technique, was performed on the dataset used in our study. Comparable performance evaluation shows that XAI-based methods choose informative bands and outperform other methods in the subsequent tasks. Thus the global and class-based effects of spectral bands can be explained, and the performance of classifiers can be improved by eliminating features that have a negative impact on classification. In HSI classification, studies examining the decisions of ML classifiers using XAI techniques are limited. Our study is one of the pioneer studies in the usage of XAI in HSI classification.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778099","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}
Chlorophyll-a (Chl-a) concentration estimation by remote sensing is an important means for monitoring offshore water quality and eutrophication. In-situ hyperspectral data can achieve accurate analyses of Chl-a, but it is not suitable for regional inversion. Satellite remote sensing provides the possibility for regional inversion, but the precision is lower limited to atmospheric correction result. Therefore, this work uses machine learning to fuse in-situ hyperspectral data and Sentinel-2 multispectral instrument images to combine their complementary advantages, so as to improve the precision of regional Chl-a concentration inversion. First, the in-situ spectra were resampled based on the satellite spectral response function to obtain equivalent reflectance. Second, the spectral feature bands of Chl-a were determined by correlation analysis. Then three machine learning models, support vector regression, random forest, and back propagation neural network, were used to establish mapping relationships of feature bands between equivalent reflectance and satellite image reflectance so as to correct the satellite feature bands. Finally, Chl-a inversion models were constructed based on the satellite feature bands before and after correction. The results demonstrate that the corrected inversion model shows an increase in R2 by 0.25 and a decrease in mean relative error by 7.6%. This fusion method effectively improves the accuracy of large-scale Chl-a concentration estimation.
{"title":"Coastal chlorophyll-a concentration estimation by fusion of Sentinel-2 multispectral instrument and in-situ hyperspectral data","authors":"Mengxue Jia, Mingming Xu, Jianyong Cui, Shanwei Liu, Hui Sheng, Zhongwei Li","doi":"10.1117/1.jrs.18.042602","DOIUrl":"https://doi.org/10.1117/1.jrs.18.042602","url":null,"abstract":"Chlorophyll-a (Chl-a) concentration estimation by remote sensing is an important means for monitoring offshore water quality and eutrophication. In-situ hyperspectral data can achieve accurate analyses of Chl-a, but it is not suitable for regional inversion. Satellite remote sensing provides the possibility for regional inversion, but the precision is lower limited to atmospheric correction result. Therefore, this work uses machine learning to fuse in-situ hyperspectral data and Sentinel-2 multispectral instrument images to combine their complementary advantages, so as to improve the precision of regional Chl-a concentration inversion. First, the in-situ spectra were resampled based on the satellite spectral response function to obtain equivalent reflectance. Second, the spectral feature bands of Chl-a were determined by correlation analysis. Then three machine learning models, support vector regression, random forest, and back propagation neural network, were used to establish mapping relationships of feature bands between equivalent reflectance and satellite image reflectance so as to correct the satellite feature bands. Finally, Chl-a inversion models were constructed based on the satellite feature bands before and after correction. The results demonstrate that the corrected inversion model shows an increase in R2 by 0.25 and a decrease in mean relative error by 7.6%. This fusion method effectively improves the accuracy of large-scale Chl-a concentration estimation.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141585408","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":"Extracting winter wheat based on multi-feature optimization of short-time series synthetic aperture radar data with dual polarizations","authors":"Kai Wang, Zhiyong Wang, Zhenjin Li, Xiaotong Liu, Huiyang Zhang, Xiangyu Zhao","doi":"10.1117/1.jrs.18.024514","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024514","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141340441","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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