Land cover monitoring using time series hyperspectral data via fractional order darwinian particle swarm optimization segmentation
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.
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)