Hyperspectral image classification using ant colony optimization algorithm based on joint spectral-spatial parameters

Classification of hyperspectral satellite images is one of the methods of extracting information which further can be used in the form of thematic maps or feature extraction. In order to improve classification accuracy along with spectral classifiers some spatial information driven preprocessing or postprocessing techniques are used. Here, we have proposed joint spatial-spectral information based classifier using Ant Colony Optimization which removes requirement of computationally hard sequential filtering. Proposed method has achieved an improvement of 5-10% in classification accuracy over traditional methods like Support Vector Machine, ANN, and SAM.