关键词: 遥感图像, K-means算法,  蜂群算法,  图像去雾,  聚类优化 

Abstract: Due to the poor defogging effect of remote sensing image clustering, the clustering accuracy and Kappa coefficient of image clustering are low and the time is long. In order to solve these problems, a new clustering optimization method remote sensing image based on adaptive K-means algorithm is proposed. Firstly, dark channel prior estimation and color line prior estimation are used to de-fog remote sensing images. Secondly, the gray co-occurrence matrix of the remote sensing image after fog removal is calculated, and the texture features are obtained. Finally, the colony algorithm is used to optimize the K-means algorithm, and the optimized adaptive K-means algorithm is used to realize the clustering optimization of remote sensing images according to texture features. The experimental results show that the proposed method can effectively eliminate cloud and fog in remote sensing images, and the image details are clearly displayed. The proposed method has good performance in terms of clustering accuracy, Kappa coefficient and clustering time. The clustering accuracy reaches 94. 9%, the Kappa coefficient is 0. 97, and the clustering time is 0.36 s. This method has certain validity. 

Key words: remote sensing images, K-means algorithm, bee colony algorithm, image defogging, cluster optimization