Pattern Recognition in Hyperspectral Images using Feedback

摘要

An important aspect of hyperspectral pattern recognition is selecting a subset of bands to perform the classification. This is generally necessary because the statistical algorithms on which classification is based need probabilistic estimates to work. The great number of spectral bands in hyperspectral images means that there is not enough data to accurately perform these estimates. In typical hyperspectral pattern recognition, the band selection and classification stages are done separately. This paper presents research done with an iterative system that integrates the band selection and classification. The objective is to choose an optimal subgroup of bands by maximizing the distance between the centroids of the classified data. The results of the study show that: (1) the algorithm correctly chooses the best bands based on centroid separability with synthetic data, (2) the system converges, and (3) the percentage of samples classified correctly using the iterative system is greater than the percentage using all the bands.