Hyperspectral Image Segmentation using Homogeneous Area Limiting and Shortest Path Algorithm

Abstract

Abstract-Segmentation, as a preprocessing, plays an important role in hyperspectral images. In this paper, considering the similarity of neighboring pixels, using the size measure, the image spectrum is divided into several segments so that the existence of several sub areas in each segment is possible. Then, using the methods of area limiting and the shortest path to seed pixel, and considering the pixel spectra in all bands, the available areas in each section are separated. The area limiting method controls the amplitude changes of area pixels from seed pixel, and the shortest path method, considering the shortest path to seed, controls the size of area. The proposed method is implemented on AVIRIS images and in terms of the number of areas, the border between areas and the possibility of area interference show better results than other methods.