In the area of remote sensing image processing, accurate segmentation of high-resolution remote sensing images in real time remains a challenging problem and numerous approaches have been developed for the problem. This paper proposes a new unsupervised approach that can efficiently analyze a remote sensing image and provide accurate segmentation results. The approach performs segmentation in three stages. In the first stage, an image is partitioned into blocks of equal sizes. The mean values of the R, G and B components of the pixels in each block are computed to form a feature vector of the block. A preliminary segmentation result is obtained by clustering the feature vectors with a simple clustering algorithm. In the second stage, a Bayesian approach is applied to refine the preliminary segmentation result. In the final stage, a graph-based method is utilized to recognize regions with complex texture structures. The performance of this approach has been tested on a few benchmark datasets, and its segmentation accuracy is compared with that of many state-of-the-art segmentation tools for remote sensing images. The testing results show that the overall segmentation accuracy of the proposed approach is higher than that of the other tools, and real-time analysis suggests that the approach is promising for real-time applications. An implementation of the approach in MATLAB is freely available upon request.

在遥感图像处理领域，高分辨率实时遥感图像的实时精确分割仍然是一个具有挑战性的问题，并且已经针对该问题开发了许多方法。本文提出了一种新的无监督方法，可以有效地分析遥感图像并提供准确的分割结果。该方法分三个阶段执行分割。在第一阶段，将图像划分为相等大小的块。计算每个块中像素的R，G和B分量的平均值，以形成该块的特征矢量。通过使用简单的聚类算法对特征向量进行聚类，可以获得初步的分割结果。在第二阶段，使用贝叶斯方法来细化初步分割结果。在最后阶段 利用基于图的方法来识别具有复杂纹理结构的区域。该方法的性能已在一些基准数据集上进行了测试，并将其分割精度与许多先进的遥感图像分割工具进行了比较。测试结果表明，该方法的整体分割精度高于其他工具，实时分析表明该方法对于实时应用是有希望的。可根据要求免费提供MATLAB中该方法的实现。测试结果表明，该方法的整体分割精度高于其他工具，实时分析表明该方法对于实时应用是有希望的。可根据要求免费提供MATLAB中该方法的实现。测试结果表明，该方法的整体分割精度高于其他工具，实时分析表明该方法对于实时应用是有希望的。可根据要求免费提供MATLAB中该方法的实现。