Two-Stage Clustering Technique Based on the Neighboring Union Histogram for Hyperspectral Remote Sensing Images

This paper presents a novel unsupervised classification approach suited to hyperspectral remote sensing image data sets that uses
$K$
-means clustering combined with the neighboring union histogram (NUH). The approach is implemented in five main steps as follows: 1) dividing the hyperspectral image data intt uncorrelated groups based on the correlation coefficient matrix; 2) extracting the first few principal component analysis (PCA) components from each group; 3) computing the NUHs of every group; 4) obtaining several relatively rough clustering results by employing the first-stage
$K$
-means procedure to classify every group’s NUHs; and 5) using the second-stage
$K$
-means procedure to refine the rough clustering results for the final clustering map. The NUH indicates the regional statistical features of a point, thereby making the proposed approach insensitive to noise and abnormal data. The two-stage clustering technique improves the recognition rate of similar land cover classes. The proposed approach is compared with five other unsupervised methods. The experimental results on two different types of real-world hyperspectral remote sensing images validate the high accuracy of the proposed approach.