GPU Projection of ECAS-II Segmenter for Hyperspectral Images Based on Cellular Automata

Abstract

Segmentation plays a crucial role in the analysis of multidimensional images, such as those used in remote sensing. Typically, segmentation algorithms for these images start by reducing their dimensionality, which can lead to the loss of potentially important information for the segmentation process. Evolutionary cellular automata segmentation (ECAS-II) offers an alternative by utilizing a cellular automata-based approach that considers all the spectral information in a hyperspectral image, without resorting to dimensionality reduction techniques. This paper introduces an efficient implementation of ECAS-II on a graphics processing unit (GPU) for segmenting hyperspectral land cover images. The proposed method is integrated into a spectral-spatial classification framework based on extreme learning machines (ELM). Experimental results indicate that this approach provides better accuracy for land cover classification compared to other segmentation-based spectral-spatial classification techniques.