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Change Detection and Image Time Series Analysis 1最新文献

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Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy 基于滤波器组和相对熵的极端环境下无监督功能信息聚类
Pub Date : 2021-12-03 DOI: 10.1002/9781119882268.ch4
A. Atto, F. Karbou, Sophie G iffard -R oisin, L. Bombrun
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引用次数: 0
Multitemporal Analysis of Sentinel‐1/2 Images for Land Use Monitoring at Regional Scale 基于Sentinel‐1/2影像的区域土地利用监测多时相分析
Pub Date : 2021-12-03 DOI: 10.1002/9781119882268.ch8
A. Garzelli, C. Zoppetti
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引用次数: 0
Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images 纹理跟踪特征点图:在SAR图像变化检测和冰川流量测量中的应用
Pub Date : 2021-12-03 DOI: 10.1002/9781119882268.ch7
M. Pham, G. Mercier
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引用次数: 0
Statistical Difference Models for Change Detection in Multispectral Images 多光谱图像变化检测的统计差分模型
Pub Date : 2021-12-03 DOI: 10.1002/9781119882268.ch9
M. Zanetti, F. Bovolo, L. Bruzzone
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引用次数: 1
Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel‐1 Image Time Series 哨兵- 1图像时间序列更好地检测山区湿雪的阈值和距离
Pub Date : 2021-12-03 DOI: 10.1002/9781119882268.ch5
F. Karbou, G. James, P. Durand, A. Atto
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Thresholds and distances to better detect wet snow over mountains with Sentinel-1 image time series Fatima Karbou, Guillaume James, Philippe Durand, Abdourrahmane Atto
它是一个多学科的开放获取档案,用于科学研究文件的存储和传播,无论它们是否出版。这些文件可能来自法国或国外的教学和研究机构,也可能来自公共或私人研究中心。HAL开放多学科档案旨在存放和传播来自法国或外国教育和研究机构、公共或私人实验室的已发表或未发表的研究级科学文件。= =地理= =根据美国人口普查,该地区的总面积为,其中土地和(1.624平方公里)水。
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引用次数: 3
Unsupervised Change Detection in Multitemporal Remote Sensing Images 多时相遥感图像的无监督变化检测
Pub Date : 2021-12-03 DOI: 10.1002/9781119882268.ch1
Sicong Liu, F. Bovolo, L. Bruzzone, Qian Du, X. Tong
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引用次数: 0
Fractional Field Image Time Series Modeling and Application to Cyclone Tracking 分数场图像时间序列建模及其在气旋跟踪中的应用
Pub Date : 2021-12-03 DOI: 10.1002/9781119882268.ch6
A. Atto, Alu'isio Pinheiro, G. Ginolhac, P. Morettin
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引用次数: 0
An Overview of Covariance‐based Change Detection Methodologies in Multivariate SAR Image Time Series 多元SAR图像时间序列中基于协方差的变化检测方法综述
Pub Date : 2021-12-03 DOI: 10.1002/9781119882268.ch3
A. Mian, G. Ginolhac, J. Ovarlez, A. Breloy, F. Pascal
Change detection (CD) for remotely sensed images of the Earth has been a popular subject of study in the past decades. It has indeed attracted a plethora of scholars due to the various applications, in both military (activity monitoring) and civil (geophysics, disaster assessment, etc.) contexts. With the increase in the number of spatial missions with embedded synthetic aperture radar (SAR) sensors, the amount of readily available observations has now reached the “big data” era. To efficiently process and analyze this data, automatic algorithms have therefore to be developed. Notably, CD algorithms have been thoroughly investigated: the literature on the subject is dense, and a variety of methodologies can be envisioned1.
近几十年来,地球遥感影像的变化检测一直是一个热门的研究课题。由于在军事(活动监测)和民用(地球物理、灾害评估等)方面的各种应用,它确实吸引了大量的学者。随着嵌入合成孔径雷达(SAR)传感器的空间任务数量的增加,现成的观测量现已进入“大数据”时代。因此,为了有效地处理和分析这些数据,必须开发自动算法。值得注意的是,CD算法已经得到了彻底的研究:关于这个主题的文献是密集的,并且可以设想各种方法1。
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引用次数: 3
期刊
Change Detection and Image Time Series Analysis 1
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