Abstract: Image segmentation is a key step in the analysis of high-resolution remote sensing images and plays an important role in improving information extraction accuracy. To improve the performance of traditional pixel-based image segmentation methods, this study proposed a new algorithm based on Parzen window density estimation of super-pixel blocks. The new algorithm includes three main steps, namely super-pixel initial segmentation, feature measurement, and density estimation and re-clustering. In the first step, an image is coarsely divided using the simple linear iterative clustering (SLIC) algorithm, and each super-pixel block is marked as a node in the graph structure of the image. In the second step, the Gabor texture features of each super-pixel block are measured to construct high-dimension feature vectors. Meanwhile, the similarity of the image textures is calculated as the weight of the edge connecting two nodes in the graph. Then, the distance between the two nodes is calculated on the minimum spanning tree (MST) of the graph. In the third step, the calculated distance is used for Parzen window density estimation of each node, and re-clustering of the density values is conducted to obtain the final results. In the experiments, multiple multispectral high-resolution remote sensing images were adopted to verify the algorithm proposed in this study. Using visual discrimination and the quantitative evaluation based on precesion rate and recall rate, the segmentation results of the algorithm proposed in this study were compared with those of other algorithms. The experiments verified that the algorithm proposed in this study is effective.