The research of unsupervised change detection method based on the clustering characteristic of 3D histogram

Abstract

One of the main problems related to unsupervised change detection methods based on the ‘difference image’ lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both the accuracy and the reliability of the change detection process. To overcome such drawbacks, in this paper, we propose an automatic techniques (based on the clustering characteristic of 3D histogram) for the analysis of the difference image. The 3D histogram is formed by pixel grey levels, contiguous average grey levels and local average grey levels of the difference image. First, the optimal plane threshold and plane direction are searched by using maximal entropy principle based on the clustering characteristic of 3D histogram. Then, by using the optimal plane threshold and plane direction, a plane is established to segment the 3D histogram into changed clustering and unchanged clustering. Finally, the changed pixels in the difference image are discriminated according to the segmentation of 3D histogram. The theoretical analysis and experiment results confirm the effectiveness of the proposed method.