Description

Remote sensing image is taken as the input and converted into the gray scale image. Then the gray scale image is filtered by using Laplacian of gaussian (LoG) filters. After that , the features are enhanced by using local spectral histogram. Then we are clustering the image using k-mean clustering. Moreover, the clustered image is segmented by using RGB colors. The SVD is calculated for error stimation and plot in the graph. The overall performance is good. Linear filters are used to provide enhanced spatial patterns. For each pixel location, we compute combined spectral and texture features using local spectral histograms, which concatenate local histograms of all input bands. We regard each feature as a linear combination of several representative features, each of which corresponds to a segment. Segmentation is given by estimating combination weights, which indicate segment ownership of pixels. We present segmentation solutions where representative features are either known or unknown. We also show that feature dimensions can be greatly reduced via subspace projection. The scale issue is investigated, and an algorithm is presented to automatically select proper scales, which does not require segmentation at multiplescale levels.