Land-cover monitoring using time-series hyperspectral data via fractional-order darwinian particle swarm optimization segmentation

Abstract

This paper presents a new method for unsupervised detection of multiple changes using time-serires hyperspectral data. The proposed method is based on fractional-order Darwinian particle swarm optimization (FODPSO) segmentation. The proposed method is applied to monitor land-cover changes following the Fukushima Daiichi nuclear disaster using multitemporal Hyperion images. Experimental results indicate that the integration of segmentation and a time-series of hyperspectral images has great potential for unsupervised detection of multiple changes.