The speed of the image clustering method based on the spectral clustering algorithm is greatly affected by the size of image resolution; in many cases, the processing of high-resolution images cannot be completed precisely on time. Thus, based on the connected area labeling and superpixel spectral clustering algorithm, this paper proposes a fast change detection method for remote sensing images based on the connected area labeling method and spectral clustering algorithm. In the initial stage, the method adopts the idea of non-local (NL)-means algorithm to generate NL difference image, then, through the OTSU method, several critical areas with large connected areas are discriminated according to a threshold, and the rectangular contour internal images of these areas are extracted and processed by the superpixel spectral clustering algorithm, to realize the change detection of the areas quickly. The results of the application experiments and the performance experiments proved that the CASC method could solve the feasibility of spectral clustering algorithm to deal with high-resolution remote sensing images to a certain extent, has good robustness, and the processing speed has been dramatically improved. The proposed method can quickly and efficiently extract sensitive sub-areas with significant changes in the study area and provide basis and decision support for geological monitoring and research in critical areas in the future.

中文翻译：

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基于连通区域标注和光谱聚类算法的遥感影像快速变化检测方法

基于光谱聚类算法的图像聚类方法的速度受图像分辨率大小的影响很大。在许多情况下，高分辨率图像的处理无法精确地按时完成。因此，基于连通区域标记和超像素光谱聚类算法，提出了一种基于连通区域标记和光谱聚类算法的遥感图像快速变化检测方法。在初始阶段，该方法采用非局部（NL）均值算法的思想来生成NL差异图像，然后通过OTSU方法，根据阈值区分出几个具有较大连通面积的关键区域，并采用矩形通过超像素光谱聚类算法提取并处理这些区域的轮廓内部图像，快速实现区域变化检测。应用实验和性能实验的结果证明，CASC方法可以在一定程度上解决光谱聚类算法处理高分辨率遥感影像的可行性，具有很好的鲁棒性，并且处理速度得到了显着提高。该方法可以快速有效地提取研究区域发生重大变化的敏感分区，为今后关键区域的地质监测和研究提供依据和决策依据。应用实验和性能实验的结果证明，CASC方法可以在一定程度上解决光谱聚类算法处理高分辨率遥感影像的可行性，具有很好的鲁棒性，并且处理速度得到了显着提高。该方法可以快速有效地提取研究区域发生重大变化的敏感分区，为今后关键区域的地质监测和研究提供基础和决策支持。应用实验和性能实验的结果证明，CASC方法可以在一定程度上解决光谱聚类算法处理高分辨率遥感影像的可行性，具有很好的鲁棒性，并且处理速度得到了显着提高。该方法可以快速有效地提取研究区域发生重大变化的敏感分区，为今后关键区域的地质监测和研究提供基础和决策支持。