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":"21 1","pages":""},"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":"20 1","pages":""},"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":"205 1","pages":""},"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}
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":"54 1","pages":""},"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}
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":"180 1","pages":""},"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}
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":"1 1","pages":""},"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}
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":"157 1","pages":""},"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}
Shenming Qu, Yongyong Lu, Can Cui, Jiale Duan, Yuan Xie
Extracting roads from complex remote sensing images is a crucial task for applications, such as autonomous driving, path planning, and road navigation. However, conventional convolutional neural network-based road extraction methods mostly rely on square convolutions or dilated convolutions in the local spatial domain. In multi-directional continuous road segmentation, these approaches can lead to poor road connectivity and non-smooth boundaries. Additionally, road areas occluded by shadows, buildings, and vegetation cannot be accurately predicted, which can also affect the connectivity of road segmentation and the smoothness of boundaries. To address these issues, this work proposes a multi-directional spatial connectivity network (MDSC-Net) based on multi-directional strip convolutions. Specifically, we first design a multi-directional spatial pyramid module that utilizes a multi-scale and multi-directional feature fusion to capture the connectivity relationships between neighborhood pixels, effectively distinguishing narrow and scale different roads, and improving the topological connectivity of the roads. Second, we construct an edge residual connection module to continuously learn and integrate the road boundaries and detailed information of shallow feature maps into deep feature maps, which is crucial for the smoothness of road boundaries. Additionally, we devise a high-low threshold connectivity algorithm to extract road pixels obscured by shadows, buildings, and vegetation, further refining textures and road details. Extensive experiments on two distinct public benchmarks, DeepGlobe and Ottawa datasets, demonstrate that MDSC-Net outperforms state-of-the-art methods in extracting road connectivity and boundary smoothness. The source code will be made publicly available at https://github/LYY199873/MDSC-Net.
{"title":"MDSC-Net: multi-directional spatial connectivity for road extraction in remote sensing images","authors":"Shenming Qu, Yongyong Lu, Can Cui, Jiale Duan, Yuan Xie","doi":"10.1117/1.jrs.18.024504","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024504","url":null,"abstract":"Extracting roads from complex remote sensing images is a crucial task for applications, such as autonomous driving, path planning, and road navigation. However, conventional convolutional neural network-based road extraction methods mostly rely on square convolutions or dilated convolutions in the local spatial domain. In multi-directional continuous road segmentation, these approaches can lead to poor road connectivity and non-smooth boundaries. Additionally, road areas occluded by shadows, buildings, and vegetation cannot be accurately predicted, which can also affect the connectivity of road segmentation and the smoothness of boundaries. To address these issues, this work proposes a multi-directional spatial connectivity network (MDSC-Net) based on multi-directional strip convolutions. Specifically, we first design a multi-directional spatial pyramid module that utilizes a multi-scale and multi-directional feature fusion to capture the connectivity relationships between neighborhood pixels, effectively distinguishing narrow and scale different roads, and improving the topological connectivity of the roads. Second, we construct an edge residual connection module to continuously learn and integrate the road boundaries and detailed information of shallow feature maps into deep feature maps, which is crucial for the smoothness of road boundaries. Additionally, we devise a high-low threshold connectivity algorithm to extract road pixels obscured by shadows, buildings, and vegetation, further refining textures and road details. Extensive experiments on two distinct public benchmarks, DeepGlobe and Ottawa datasets, demonstrate that MDSC-Net outperforms state-of-the-art methods in extracting road connectivity and boundary smoothness. The source code will be made publicly available at https://github/LYY199873/MDSC-Net.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"22 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885493","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}
Christ Alain Nekuie Mouafo, Charles Antoine Basseka, Suzanne Ngo Boum Nkot, Constantin Mathieu Som Mbang, Cyrille Donald Njiteu Tchoukeu, Yannick Stephan Kengne, Paul Bertrand Tsopkeng, Jacques Etame
The aim of this study is to map and analyze the lineament network in the Edéa, Cameroon, area using remote sensing and gravimetric data to determine their hydrogeological implications. Principal component analysis and directional filters applied to Landsat7 ETM+ and Shuttle Radar Topography Mission imagery, respectively, were used to extract remote sensing lineaments. Rose diagram of these lineaments highlights four families of lineaments along the N–S, E–W, NE–SW, and NW–SE directions. There are three major directions accounting for 74% of lineaments, including N0° to N10°, N20° to N30°, and N40° to N50°; and four minor directions (with 26% of the lineaments), including N60° N70°, N80° to N90°, N130° to N140°, and N150° to N160°. N20° to N90° directions correlate with those of major structures of the Oubanguides Complex, such as the Sanaga Fault and Central Cameroon Shear Zone. N130° to N140° direction corresponds to orientation of Shear Zones and blastomylonitic faults of Nyong Complex. Superposition of these lineaments on hydrographic network shows similarities between their directions, thus highlighting strong impact of tectonics on orientation of hydrographic network. The presence of numerous lineaments highlights strongly fractured subsoil, and their high density favors the circulation and accumulation of groundwater. Upward continuation and horizontal gradient maxima methods applied to Earth Gravitational Model 2008 data allowed the extraction of gravimetric lineaments, with a major N–S orientation, which correlates with general orientation of South Atlantic opening. Superposition of remote sensing lineaments and gravimetric lineaments highlights their parallelism, admitting that gravimetric structures are an extension in depth of surface structures defined by remote sensing.
{"title":"Lineament mapping in the Edea area (Littoral, Cameroon) using remote sensing and gravimetric data: hydrogeological implications","authors":"Christ Alain Nekuie Mouafo, Charles Antoine Basseka, Suzanne Ngo Boum Nkot, Constantin Mathieu Som Mbang, Cyrille Donald Njiteu Tchoukeu, Yannick Stephan Kengne, Paul Bertrand Tsopkeng, Jacques Etame","doi":"10.1117/1.jrs.18.032402","DOIUrl":"https://doi.org/10.1117/1.jrs.18.032402","url":null,"abstract":"The aim of this study is to map and analyze the lineament network in the Edéa, Cameroon, area using remote sensing and gravimetric data to determine their hydrogeological implications. Principal component analysis and directional filters applied to Landsat7 ETM+ and Shuttle Radar Topography Mission imagery, respectively, were used to extract remote sensing lineaments. Rose diagram of these lineaments highlights four families of lineaments along the N–S, E–W, NE–SW, and NW–SE directions. There are three major directions accounting for 74% of lineaments, including N0° to N10°, N20° to N30°, and N40° to N50°; and four minor directions (with 26% of the lineaments), including N60° N70°, N80° to N90°, N130° to N140°, and N150° to N160°. N20° to N90° directions correlate with those of major structures of the Oubanguides Complex, such as the Sanaga Fault and Central Cameroon Shear Zone. N130° to N140° direction corresponds to orientation of Shear Zones and blastomylonitic faults of Nyong Complex. Superposition of these lineaments on hydrographic network shows similarities between their directions, thus highlighting strong impact of tectonics on orientation of hydrographic network. The presence of numerous lineaments highlights strongly fractured subsoil, and their high density favors the circulation and accumulation of groundwater. Upward continuation and horizontal gradient maxima methods applied to Earth Gravitational Model 2008 data allowed the extraction of gravimetric lineaments, with a major N–S orientation, which correlates with general orientation of South Atlantic opening. Superposition of remote sensing lineaments and gravimetric lineaments highlights their parallelism, admitting that gravimetric structures are an extension in depth of surface structures defined by remote sensing.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"27 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140932728","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}
Early disease detection is required, considering the impacts of diseases on crop yield. However, current methods involve labor-intensive data collection. Thus, unsupervised anomaly detection in time series imagery was proposed, requiring high-resolution unmanned aerial vehicle (UAV) imagery and sophisticated algorithms to identify unknown anomalies amidst complex data patterns to cope with within season crop monitoring and background challenges. The dataset used in this study was acquired by a Micasense Altum sensor on a DJI Matrice 210 UAV with a 4 mm resolution in Gottingen, Germany. The proposed methodology includes (1) date selection for finding the date sensitive to sugar beet changes, (2) vegetation index (VI) selection for finding the one sensitive to sugar beet and its temporal patterns by visual inspection, (3) sugar beet extraction using thresholding and morphological operator, and (4) an ensemble of bottom-up, Kernel, and quadratic discriminate analysis methods for unsupervised time series anomaly detection. The study highlighted the importance of the wide-dynamic-range VI and morphological filtering with time series trimming for accurate disease detection while reducing background errors, achieving a kappa of 76.57%, comparable to deep learning model accuracies, indicating the potential of this approach. Also, 81 days after sowing, image acquisition could begin for cost and time efficient disease detection.
{"title":"Cercospora leaf spot detection in sugar beets using high spatio-temporal unmanned aerial vehicle imagery and unsupervised anomaly detection methods","authors":"Helia Noroozi, Reza Shah-Hosseini","doi":"10.1117/1.jrs.18.024506","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024506","url":null,"abstract":"Early disease detection is required, considering the impacts of diseases on crop yield. However, current methods involve labor-intensive data collection. Thus, unsupervised anomaly detection in time series imagery was proposed, requiring high-resolution unmanned aerial vehicle (UAV) imagery and sophisticated algorithms to identify unknown anomalies amidst complex data patterns to cope with within season crop monitoring and background challenges. The dataset used in this study was acquired by a Micasense Altum sensor on a DJI Matrice 210 UAV with a 4 mm resolution in Gottingen, Germany. The proposed methodology includes (1) date selection for finding the date sensitive to sugar beet changes, (2) vegetation index (VI) selection for finding the one sensitive to sugar beet and its temporal patterns by visual inspection, (3) sugar beet extraction using thresholding and morphological operator, and (4) an ensemble of bottom-up, Kernel, and quadratic discriminate analysis methods for unsupervised time series anomaly detection. The study highlighted the importance of the wide-dynamic-range VI and morphological filtering with time series trimming for accurate disease detection while reducing background errors, achieving a kappa of 76.57%, comparable to deep learning model accuracies, indicating the potential of this approach. Also, 81 days after sowing, image acquisition could begin for cost and time efficient disease detection.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"2 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933053","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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