首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >CHANGE DETECTION OF HIGH-RESOLUTION REMOTE SENSING IMAGES BASED ON ADAPTIVE FUSION OF MULTIPLE FEATURES
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CHANGE DETECTION OF HIGH-RESOLUTION REMOTE SENSING IMAGES BASED ON ADAPTIVE FUSION OF MULTIPLE FEATURES

机译:基于多种特征自适应融合的高分辨率遥感影像变化检测

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In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.
机译:鉴于传统的变化检测算法主要依靠光谱信息图像斑点,未能有效挖掘和融合多图像特征检测优势,本文借鉴了面向对象分析的思想,提出了一种多特征融合的遥感图像变化方法。检测算法。首先以图像对象的多尺度分割为基础;然后计算颜色直方图和线性梯度直方图的各种对象;利用对象不同时期EMD统计算子之间的颜色距离和边缘线特征距离,采用自适应加权方法,将颜色特征距离和边缘在直线距离上的组合构造为对象异质性。最后,获得曲率直方图分析图像斑点变化的检测结果。实验结果表明,该方法可以充分融合颜色和边缘线特征,从而提高了变化检测的准确性。

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