Yao Huang, Rui Wang, Ming Ju, Xianxun Zhu, Yanan Xie
The multisource satellite observation data have been widely used in carbon cycle research owing to their long-term and large-scale characteristics. However, the sparse sampling density of satellite observation data often results in incomplete spatiotemporal coverage at certain time intervals, which hinders the accurate representation of global carbon dioxide (CO2) concentration variations and is inadequate for supporting research applications with different precision requirements. To address this issue, a new multiscale fixed rank kriging is proposed to generate long-term daily scale column-averaged dry-air mole fraction of CO2 (XCO2) products from 2016 to 2019 over the globe on grids of 1°, for which the XCO2 data from Orbiting Carbon Observatory-2, Orbiting Carbon Observatory-3, and Greenhouse gases Observing SATellite are applied. Experimental results show that the dataset has a high spatiotemporal resolution and coverage validated by the Total Carbon Column Observing Network data to effectively fill gaps in satellite observation data, with cross-validation of R2=0.93 and root mean square error = 1.06 ppm. Moreover, we analyze the spatial distribution and seasonal variation characteristics of global and Chinese XCO2 from 2016 to 2019, with XCO2 presenting an obvious latitudinal gradient and seasonal periodicity in space. The proposed method establishes a foundational research dataset for the analysis of spatiotemporal variation characteristics of CO2 concentration at global and regional scales, as well as investigations on carbon sources and sink.
{"title":"Reconstructing global daily XCO2 at 1° × 1° spatial resolution from 2016 to 2019 with multisource satellite observation data","authors":"Yao Huang, Rui Wang, Ming Ju, Xianxun Zhu, Yanan Xie","doi":"10.1117/1.jrs.18.028502","DOIUrl":"https://doi.org/10.1117/1.jrs.18.028502","url":null,"abstract":"The multisource satellite observation data have been widely used in carbon cycle research owing to their long-term and large-scale characteristics. However, the sparse sampling density of satellite observation data often results in incomplete spatiotemporal coverage at certain time intervals, which hinders the accurate representation of global carbon dioxide (CO2) concentration variations and is inadequate for supporting research applications with different precision requirements. To address this issue, a new multiscale fixed rank kriging is proposed to generate long-term daily scale column-averaged dry-air mole fraction of CO2 (XCO2) products from 2016 to 2019 over the globe on grids of 1°, for which the XCO2 data from Orbiting Carbon Observatory-2, Orbiting Carbon Observatory-3, and Greenhouse gases Observing SATellite are applied. Experimental results show that the dataset has a high spatiotemporal resolution and coverage validated by the Total Carbon Column Observing Network data to effectively fill gaps in satellite observation data, with cross-validation of R2=0.93 and root mean square error = 1.06 ppm. Moreover, we analyze the spatial distribution and seasonal variation characteristics of global and Chinese XCO2 from 2016 to 2019, with XCO2 presenting an obvious latitudinal gradient and seasonal periodicity in space. The proposed method establishes a foundational research dataset for the analysis of spatiotemporal variation characteristics of CO2 concentration at global and regional scales, as well as investigations on carbon sources and sink.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593513","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}
Due to the complexity of hyperspectral data and the scarcity of labeled samples, unsupervised clustering segmentation has become a hot spot of interest in remote sensing. Sparse subspace clustering (SSC) is the most common clustering approach at the moment, although its computational cost restricts its use on big remote sensing datasets. Furthermore, SSC’s neglect of spatial information and limited recognition ability hinder the spatial homogeneity of clustering results. Hence, this work proposes a fast spectral clustering algorithm for local cosine similarity graphs. First, the fuzzy simple linear iterative clustering superpixel method is introduced into the SSC framework to treat superpixels as homogeneous entities and obtain global similarity maps using very low computational and spatial overheads. Then, a cosine similarity measure that combines spectral information and spatial information is used to obtain a local similarity graph, which enhances the accuracy of the final classification and suppresses noise. Extensive testing demonstrates the value of the proposed method. Compared to state-of-the-art SSC-based algorithms, it offers superior classification performance, noise immunity, and very little computational overhead.
{"title":"Fast spectral clustering with local cosine similarity graphs for hyperspectral images","authors":"Zhenxian Lin, Yuheng Jiang, Chengmao Wu","doi":"10.1117/1.jrs.18.024502","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024502","url":null,"abstract":"Due to the complexity of hyperspectral data and the scarcity of labeled samples, unsupervised clustering segmentation has become a hot spot of interest in remote sensing. Sparse subspace clustering (SSC) is the most common clustering approach at the moment, although its computational cost restricts its use on big remote sensing datasets. Furthermore, SSC’s neglect of spatial information and limited recognition ability hinder the spatial homogeneity of clustering results. Hence, this work proposes a fast spectral clustering algorithm for local cosine similarity graphs. First, the fuzzy simple linear iterative clustering superpixel method is introduced into the SSC framework to treat superpixels as homogeneous entities and obtain global similarity maps using very low computational and spatial overheads. Then, a cosine similarity measure that combines spectral information and spatial information is used to obtain a local similarity graph, which enhances the accuracy of the final classification and suppresses noise. Extensive testing demonstrates the value of the proposed method. Compared to state-of-the-art SSC-based algorithms, it offers superior classification performance, noise immunity, and very little computational overhead.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593749","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}
Deep neural network-based synthetic aperture radar (SAR) image change detection algorithms are affected by coherent speckle noise in the original image. Existing denoising methods have predominantly focused on generating binary images based on the pre-classification of original pixels, which is insufficient in removing interfering noise. Herein, to further reduce the noise points generated in the clustering algorithm, we combined the characteristics of the fuzzy clustering algorithm, demonstrating the obvious advantages of the proposed fast and flexible denoising convolutional neural network (FFDNet-F) method. An FFDNet was used to reduce noise interference in real SAR images and improve the detection accuracy and robustness of the method. Difference operators were then drawn from the weak noise images, and fuzzy local information C-means clustering was applied for analysis to generate the change detection results. The experimental results from two real datasets and the comparative analysis with other network models demonstrated the effectiveness of this method. Simultaneously, Gaofen-3 satellite images were used to verify and analyze surface flood disasters in Zhengzhou, China. The findings of this study demonstrate a significant improvement in detection accuracy using the proposed method compared with that of other algorithms.
{"title":"Synthetic aperture radar image change detection based on image difference denoising and fuzzy local information C-means clustering","authors":"Yuqing Wu, Qing Xu, Xinming Zhu, Tianming Zhao, Bowei Wen, Jingzhen Ma","doi":"10.1117/1.jrs.18.024501","DOIUrl":"https://doi.org/10.1117/1.jrs.18.024501","url":null,"abstract":"Deep neural network-based synthetic aperture radar (SAR) image change detection algorithms are affected by coherent speckle noise in the original image. Existing denoising methods have predominantly focused on generating binary images based on the pre-classification of original pixels, which is insufficient in removing interfering noise. Herein, to further reduce the noise points generated in the clustering algorithm, we combined the characteristics of the fuzzy clustering algorithm, demonstrating the obvious advantages of the proposed fast and flexible denoising convolutional neural network (FFDNet-F) method. An FFDNet was used to reduce noise interference in real SAR images and improve the detection accuracy and robustness of the method. Difference operators were then drawn from the weak noise images, and fuzzy local information C-means clustering was applied for analysis to generate the change detection results. The experimental results from two real datasets and the comparative analysis with other network models demonstrated the effectiveness of this method. Simultaneously, Gaofen-3 satellite images were used to verify and analyze surface flood disasters in Zhengzhou, China. The findings of this study demonstrate a significant improvement in detection accuracy using the proposed method compared with that of other algorithms.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593754","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":"Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas","authors":"Boxin Li, Hong-e Ren, Pinliang Dong, Jing Tian","doi":"10.1117/1.jrs.18.014531","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014531","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140376515","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. Maltese, Francesco Caldareri, G. Dardanelli, Simona Todaro, N. Parrino, A. Sulli
{"title":"On the shoreline positioning via remote sensing imagery: an isoradiometric approach","authors":"A. Maltese, Francesco Caldareri, G. Dardanelli, Simona Todaro, N. Parrino, A. Sulli","doi":"10.1117/1.jrs.18.014529","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014529","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380114","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":"Forest stand segmentation with multi-temporal Sentinel-2 imagery and superpixels","authors":"C. Demirpolat, U. Leloglu","doi":"10.1117/1.jrs.18.014530","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014530","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380897","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}
Hang An, Xiaoxuan Chen, Lin Wang, Baopu Hou, Zhichao Jin, Na Meng, Bo Jiang, Yaowei Li
{"title":"Deblurring method for remote sensing image via dual scale parallel spatial fusion network","authors":"Hang An, Xiaoxuan Chen, Lin Wang, Baopu Hou, Zhichao Jin, Na Meng, Bo Jiang, Yaowei Li","doi":"10.1117/1.jrs.18.014527","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014527","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140219252","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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