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Automatic registration of remote sensing images based on SIFT and fuzzy block matching for change detection

机译:基于SIFT和模糊块匹配改变检测的遥感图像自动注册

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摘要

This paper presents an automated image registration approach to detecting changes in multi-temporal remote sensing images. The proposed algorithm is based on the scale invariant feature transform (SIFT) and has two phases. The first phase focuses on SIFT feature extraction and on estimation of image transformation. In the second phase, Structured Local Binary Haar Pattern (SLBHP) combined with a fuzzy similarity measure is then used to build a new and effective block similarity measure for change detection. Experimental results obtained on multi-temporal data sets show that compared with three mainstream block matching algorithms, the proposed algorithm is more effective in dealing with scale, rotation and illumination changes.
机译:本文介绍了检测多时间遥感图像的变化的自动图像登记方法。所提出的算法基于秤不变特征变换(SIFT)并具有两个阶段。第一阶段侧重于SIFT特征提取及图像变换估计。然后,在第二阶段,将结构化局部二进制HAAR模式(SLBHP)与模糊相似度量组合使用,为改变检测构建一个新的和有效的块相似度测量。在多时间数据集上获得的实验结果表明,与三个主流块匹配算法相比,所提出的算法在处理规模,旋转和照明变化方面更有效。

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