针对遥感影像感兴趣区域检测中所需的全局搜索与建立先验知识库等问题，提出基于显著特征聚类的遥感图像感兴趣区域检测算法。利用色彩信息，在不同的颜色通道(RGB)构建直方图以计算不同颜色通道的信息图，融合得到单幅图的显著图。接着通过k-means在CIELab颜色空间上进行聚类，在簇的层级上计算显著值，以降低计算复杂度，从而获得CIELab颜色空间的显著图。将单幅显著图与CIELab空间显著图对应融合，得到最终显著图。根据获得的最终显著图构建感兴趣区域掩膜，以达到将感兴趣区域分割出来的目的。实验结果表明，该算法不需要建立先验知识库，获得显著图结果更加准确，对遥感图像的显著性区域检测有实际意义。

The region of interest detection for remote sensing images is usually based on global research and setting up the basis of prior knowledge. The new method called salient region detection based on salient features clusting for remote sensing images is proposed. We use the color information to construct the histograms in different color channel (RGB) to compute the information maps in each color channel. After fusing the information maps, we can get the single saliency maps. To get the saliency maps in CIELab color space, we adopt the k-means to cluster all the images in the CIELab color space, which makes it possible to reduce the computational complexity by calculating saliency on cluster-level. Then, through studying the integration of single saliency map and CIELab saliency maps, we get the final saliency maps. Finally, we can construct the mask of region of interest according to the final saliency map, which enable us to get the region of interest segmentation. Result shows that compared with existing models, we get more accurate saliency maps without the basis of prior knowledge. This method will be meaningful in further remote sensing image processing.