In order to optimize the insufficient ability for complex multi-target remote sensing image detection using the reported saliency algorithms, an extraction algorithm of salient object based on similarity measurement for superpixel regions is proposed. The original image is segmented into certain superpixel regions using simple linear iterative clustering method, and some high saliency regions are extracted correctly using graph-based visual saliency method. Meanwhile, parts of the edge superpixels need to be amended and the rest of salient superpixels are used as training samples. By calculating the similarity of all superpixels and training samples hierarchically, a reasonable membership value of each superpixel is established to separate the goal superpixel regions with high saliency. Finally, all the superpixels salient objects from the original images are extracted successfully using the membership values. The experimental results show that the proposed algorithm has higher precision and recall rates than the other saliency detection methods, thus it can be effectively applied to complicated multi-objective in target information extraction of remote sensing images significantly.