Unsupervised Classification of Hyperspectral Images based on Spectral Features

Abstract

In this world of Big Data, large quantities of data are been created everyday from all the type of visual sensors available in the hands of mankind. One important data is that we obtain from satellite of the land image. The application of these data are numerous. They have been used in classification of land regions, change detection of an area over a period of time, detecting different anomalies in the area and so on. As data is increasing at a high rate, so manually doing these jobs is not a good idea. So, we have to apply automated algorithms to solve these jobs. The images we see generally consists of visible light in Red, Green and Blue bands, but light of different wavelength differ in their properties of passing obstacle. So, there has been considerable research going on continuous spectra images. These images are called Hyper-spectral Image. In this thesis, I have gone through many classic machine learning algorithms like K-means, Expectation Maximization, Hierarchical Clustering, some out of box methods like Unsupervised Artificial DNA Classifier, Spatial Spectral Information which integrates both features to get better classification and a variant of Maximal Margin Clustering which uses K-Nearest Neighbor algorithm to cross validate and get the best set to separate. Sometimes PCA is used get best features from the dataset. Finally all the results are compared