Pixel Based Classification of Remotely Sensed Image using K- means and BPNN

This paper aims at performing unsupervised and supervised classification techniques on remotely sensed image and comparing their classification accuracy. The classification techniques used are, k-means for unsupervised and Back Propagation Neural Network (BPNN) for supervised. Remotely sensed images captured by Earth observing satellites have many spectral bands from electromagnetic spectrum. LANDSAT image with seven spectral bands is used as input image to both the classifiers. For BPNN, a multilayered architecture, having one input layer, one hidden layer and one output layer is used. Samples from each class are selected to train the network and pixel based classification is performed as each pixel is tested on the trained network and is classified to one of the classes. k-means is performed by arbitrarily selecting class centers or centroids from the image and then assigning each pixel to one of these classes based on its distance from the centroid [4]. Classification accuracy of both techniques is compared and it is observed based on the experiment results that BPNN performed better than k- means.