自适应差分进化的遥感影像自动模糊聚类方法

遥感影像模糊聚类方法可以在无需样本分布信息的情况下获取比硬聚类方法更高的分类精度，但其仍依赖先验知识来确定影像地物的类别数。本文提出了一种基于自适应差分进化的遥感影像自动模糊聚类方法，该方法利用差分进化搜索速度快、计算简单、稳定性高的优点，以Xie-Beni指数为优化的适应度函数，在无需先验类别信息的情况下自动判定图像的类别数，并结合局部搜索算子对遥感影像进行最优化聚类。通过模拟影像以及两幅真实遥感图像的分类实验表明，本文方法不仅可以正确地自动获取地物类别数，而且能够获得比K均值、ISODATA以及模糊K均值方法更高的分类精度。

An Automatic Fuzzy Clustering Algorithm Based on Self-adaptive Differential Evolution for Remote Sensing Image

Fuzzy clustering method can get higher classification accuracy than the hard clustering, but it still relies on the prior assumptions on the number of clusters. This paper proposes an automatic fuzzy clustering method based on self-adaptive differential evolution for remote sensing image (AFCDE). The proposed AFCDE algorithm can adaptively find the optimal number of clusters and obtain the satisfied classification result based on Xie-Beni index by utilizing the fast, robust and efficient global search algorithm, differential evolution (DE) algorithm. Three experimental results with one simulated image and two real remote sensing images show that the proposed algorithm not only finds the optimal number of clusters, but also outperforms the traditional clustering algorithms, such as K-means, ISODATA and fuzzy K-means.