Studies on Human Skin Extraction from Hyperspectral Data using Particle Swarm Optimization

Abstract

If the automatic detection of the victims is feasible after large-scale disasters, the search efficiency would be significantly improved compared to the traditional visual search method from aircraft. In order to extract the human skin correctly, we focus on the training of an Artificial Neural Network (ANN) by the hyperspectral data. From the standpoint of avoiding the over-fitting problem generated by multi-channel inputs of the hyperspectral data, it is necessary to be trained by the optimal bands. In this paper, we propose the coupled search method for the optimal number and combination of bands in order to extract the target from the hyperspectral data. The coupled search method is composed of two search methods. The first is a new search method of the maximum value of the evaluation function which searches the optimal number of bands from the non-training data. The other is the search method using the Particle Swarm Optimization which searched the optimal combination of bands from the training data. The ANN is trained by the selected combination of bands, and the results are evaluated. Moreover, the trained network obtained using the coupled search method is compared with the ANN trained by all the bands and the Normalized Difference Human Index.