Digital Pattern Recognition by Moments
Journal of the ACM (JACM)
High-resolution landform classification using fuzzy k-means
Fuzzy Sets and Systems - Special issue on Uncertainty in geographic information systems and spatial data
Computer and Robot Vision
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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There is increasing need for effective delineation of meaningfully different landforms due to the decreasing availability of experienced landform interpreters. Any procedure for automating the process of landform segmentation from satellite images offer the promise of improved consistency and reliality. We propose a hierarchical method for landform classification for classifying a wide variety of landforms. At stage 1 an image is classified as one of the three broad categories of terrain types in terms of its geomorphology, and these are: desertic/rann of kutch, coastal or fluvial. At stage 2, all different landforms within either desertic/rann of kutch , coastal or fluvial areas are identified using suitable processing. At the final stage, all outputs are fused together to obtain a final segmented output. The proposed technique is evaluated on large number of optical band satellite images that belong to aforementioned terrain types.