ALCM: Automatic Land Cover Mapping

It may be quite important for resource management people to extract single land cover class, at sub-pixel level from multi-spectral remote sensing images of different areas in single step processing. It has been observed, that neural network can be trained to extract single land cover class from multi-spectral remote sensing images, but they have problems in setting various parameters and slow during training stage. This paper present single land cover class water, extraction from mixed pixels present in multiple multi-spectral remote sensing data sets of same bands of AWiFS sensor of Resoursesat-1 (IRS-P6) satellite from different areas. In this work fuzzy logic-based algorithm, which is independent of statistical distribution assumption of data, has been studied at sub-pixel level to handle mixed pixels. It has been found; possibilistic c-means (PCM) algorithm takes the possibilistic view, that the membership of a feature vector in a class has nothing to do with its membership in other classes. Due to this, it was observed that PCM can extract only one class, from remote sensing multi-spectral data and it has produced 93.7% and 97.1% overall sub-pixel classification accuracy for two different data sets of different places using LISS-III (IRS-P6) reference data of same dates as of AWiFS data.