Spectral slopes for automated classification of land cover in landsat images

In the literature, various techniques for supervised/ semi-supervised classification of satellite imageries require manual selection of samples for each class. In this paper, we propose a spectral-slope based classification technique, which automates the process of initial labeling of a set of sample points. These are subsequently used in a supervised classifier as training samples and it performs the task of classification over all the pixels in the image. We demonstrate the effectiveness of our proposed classification technique in summarizing the changes in temporal image sets. For selecting the training samples from the satellite imageries, a set of rules is proposed by using the spectral-slope properties. We classify the land-cover into three classes, namely, water, vegetation, and vegetation-void, and validate the classification results using very high resolution satellite imagery. The approach has also been used in the analysis of images acquired by different sensors operating under similar wavelength ranges.