Journal of Applied Remote Sensing最新文献_第4页

Reconstructing global daily XCO2 at 1° × 1° spatial resolution from 2016 to 2019 with multisource satellite observation data 利用多源卫星观测数据，以 1° × 1° 的空间分辨率重构 2016 至 2019 年全球每日 XCO2

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-04-01 DOI: 10.1117/1.jrs.18.028502

Yao Huang, Rui Wang, Ming Ju, Xianxun Zhu, Yanan Xie

{"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}

引用次数: 0

Fast spectral clustering with local cosine similarity graphs for hyperspectral images 利用局部余弦相似性图对高光谱图像进行快速光谱聚类

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-04-01 DOI: 10.1117/1.jrs.18.024502

Zhenxian Lin, Yuheng Jiang, Chengmao Wu

{"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}

引用次数: 0

Synthetic aperture radar image change detection based on image difference denoising and fuzzy local information C-means clustering 基于图像差分去噪和模糊局部信息 C-means 聚类的合成孔径雷达图像变化检测

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-04-01 DOI: 10.1117/1.jrs.18.024501

Yuqing Wu, Qing Xu, Xinming Zhu, Tianming Zhao, Bowei Wen, Jingzhen Ma

{"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}

引用次数: 0

RS-YOLOx: target feature enhancement and bounding box auxiliary regression based object detection approach for remote sensing RS-YOLOx：基于目标特征增强和边界框辅助回归的遥感物体检测方法

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-03-29 DOI: 10.1117/1.jrs.18.016514

Bao Liu, Wenqian Jiang

引用次数: 0

Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas 卷积神经网络与支持向量机在识别森林类型和烧毁区域方面的比较

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-03-27 DOI: 10.1117/1.jrs.18.014531

Boxin Li, Hong-e Ren, Pinliang Dong, Jing Tian

引用次数: 0

On the shoreline positioning via remote sensing imagery: an isoradiometric approach 通过遥感图像进行海岸线定位：等方位测量法

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-03-26 DOI: 10.1117/1.jrs.18.014529

A. Maltese, Francesco Caldareri, G. Dardanelli, Simona Todaro, N. Parrino, A. Sulli

引用次数: 0

Forest stand segmentation with multi-temporal Sentinel-2 imagery and superpixels 利用多时 Sentinel-2 图像和超像素分割林分

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-03-26 DOI: 10.1117/1.jrs.18.014530

C. Demirpolat, U. Leloglu

引用次数: 0

Assessment of asphalt pavement aging condition based on GF-2 high-resolution remote sensing image 基于 GF-2 高分辨率遥感图像的沥青路面老化状况评估

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-03-23 DOI: 10.1117/1.jrs.18.014528

Han Wang, Dayong Yang, Zhiwei Xie, Jingwen Wang, Zhigang Hao, Fanyu Zhou, Xiaona Wang

引用次数: 0

Multilevel feature aggregation and enhancement network for remote sensing change detection 用于遥感变化探测的多级特征聚合和增强网络

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-03-23 DOI: 10.1117/1.jrs.18.016513

Wenkai Yan, Yikun Liu, Mingsong Li, Ruifan Zhang, Gongping Yang

引用次数: 0

Deblurring method for remote sensing image via dual scale parallel spatial fusion network 通过双尺度并行空间融合网络为遥感图像去模糊的方法

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-03-22 DOI: 10.1117/1.jrs.18.014527

Hang An, Xiaoxuan Chen, Lin Wang, Baopu Hou, Zhichao Jin, Na Meng, Bo Jiang, Yaowei Li

引用次数: 0