Hyperspectral Image Classification Using K-means Clustering

Abstract

Hyperspectral Image stores the reflectance of objects across the electromagnetic spectrum. Each object is identified by its spectral signature. Hyperspectral Sensors records these images from airborne devices. By processing these Images we can get various information about the land-form, seabed etc. This thesis presents an efficient and accurate classification technique for Hyperspectral Images. The approach consists of three steps. Firstly, dimension reduction of the Hyperspectral Image using Principal Component Analysis. This is done in order to reduce the time complexity of the further process. Secondly, the reduced features are clustered by k-means clustering. Lastly, the clusters are individually trained by Support Vector Machine. This scheme was tested with Pavia University Data-set taken by ROSIS sensor. Using the above scheme overall accuracy of 90.2% was achieved which is very promising in comparison to conventional Support Vector Machine classification which had an overall accuracy of 78.67% with the same data-set.