The Automatic Detection of Fire Scar in Alaska using Multi-Temporal PALSAR Polarimetric SAR Data

Jujie WeiInstitute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing, China;

Yonghong ZhangInstitute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing, China; Correspondence[email protected]

Hong’an WuInstitute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing, China;

Bin CuiSchool of Geodesy and Geomatics, Wuhan University, Wuhan, China; ;Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing, China; ;Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing, China

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Jujie Wei, Yonghong Zhang, Hong’an Wu & Bin Cui. (2020) An Efficient Change Detection for Large SAR Images Based on Modified U-Net Framework. Canadian Journal of Remote Sensing 46:3, pages 272-294.
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Age Shama, Rui Zhang, Runqing Zhan, Ting Wang, Lingxiao Xie, Xin Bao & Jichao Lv. (2023) A Burned Area Extracting Method Using Polarization and Texture Feature of Sentinel-1A Images. IEEE Geoscience and Remote Sensing Letters 20, pages 1-5.
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Janine Florath & Sina Keller. (2022) Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area. Remote Sensing 14:3, pages 657.
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Xiaoping Yu & Xijuan Yue. (2022) Similarity Matrix Entropy for Multitemporal Polarimetric SAR Change Detection. IEEE Geoscience and Remote Sensing Letters 19, pages 1-5.
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The Automatic Detection of Fire Scar in Alaska using Multi-Temporal PALSAR Polarimetric SAR Data

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The Automatic Detection of Fire Scar in Alaska using Multi-Temporal PALSAR Polarimetric SAR Data

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