| 小波系数插值支持下的遥感影像混合像元分解 |
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| 引用本文： | 李熙,陈锋锐,于之锋.小波系数插值支持下的遥感影像混合像元分解[J].应用科学学报,2012,30(6):613-618. |
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| 作者姓名： | 李熙 陈锋锐 于之锋 |
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| 作者单位： | 1. 武汉大学测绘遥感信息工程国家重点实验室，武汉430079
2. 河南大学环境与规划学院，河南开封475004
3. 杭州师范大学遥感与地球科学研究院，杭州311121 |
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| 基金项目： | 国家"863"高技术研究发展计划基金，国家自然科学基金，教育部博士点基金，中央高校基本科研业务费专项资金，杭州师范大学遥感与地球科学研究院开放基金，中国博士后科学基金 |
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| 摘 要： | 针对遥感混合像元分解中不能有效利用空间邻域信息的问题，提出一种基于超分辨率重建的分解方法. 通过小波系数双线性插值获得遥感影像的超分辨率影像，对超分辨率影像进行监督分类生成超分辨率分类图，最后通过窗口统计得到原始分辨率下各地物的丰度图. 广州城区的模拟TM遥感影像试验表明，该方法的分解精度
在3种方法中最优，能够较充分利用空间邻域信息，提高混合像元分解精度，为混合像元分解提供了新的途径.
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| 关 键 词： | 遥感 混合像元 小波变换 超分辨率重建 双线性插值 |
| 收稿时间： | 2011-11-24 |
| 修稿时间： | 2012-01-22 |
Spectral Unmixing of Remote Sensing Images Using Interpolation of Wavelet Coefficients |
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| LI Xi
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CHEN Feng-rui
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YU Zhi-feng.Spectral Unmixing of Remote Sensing Images Using Interpolation of Wavelet Coefficients[J].Journal of Applied Sciences,2012,30(6):613-618. |
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| Authors: | LI Xi CHEN Feng-rui YU Zhi-feng |
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| Institution: | 1．State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. College of Environment and Planning, Henan University, Kaifeng 475004, Henan Province, China;
3．Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, China |
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| Abstract: | This paper proposes a wavelet coefficient interpolation method, which uses the neighboring information in the spatial domain for spectral unmixing of remote sensing images. A super-resolution image is first generated using bilinear interpolation of wavelet coefficients. The new image is then classified to derive
a super-resolution classification map. Finally, an abundance map at the original spatial resolution is obtained using a counting window on the super-resolution classification map. This way, the original image is unmixed. A simulated TM image of Guangzhou City is used to verify the proposed method. It is found that the method performs best among three methods as it can make use of neighboring information in the space to improve unmixing accuracy. |
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| Keywords: | remote sensing mixed pixel wavelet transformation super resolution reconstruction bilinear interpolation |
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