A Novel Ant Colony Optimization Based Training Subset Selection Algorithm for Hyperspectral Image Classification

Hyperspectral images captured by airborne sensors provide huge information about earth cover. Human eye can see reflectance from limited wavelengths whereas hyperspectral images contain more than 200 bands captured in visible and infrared region of light. Classification is one of the methods used to analyse this information for decision making. Several techniques are available in the literature for supervised, unsupervised, and semi-supervised classification of hyperspectral images. Training samples for supervised classification are generated by ground sample collection from the actual site captured in the image. These training samples play an important role in classification and can greatly influence classification accuracy. In recent research, for selecting training samples `pixel purity' based new methods were developed. However, in some cases, it might not be possible to gather ground truth of selected pixel. This paper advocate for a novel method inspired by Ant Colony System for selection of a subset from already developed training samples to achieve better classification results. It is interesting to see that a small subset of existing training samples achieve equivalent or in some cases better accuracy when compared to the use of all available training samples.