By Topic

A Hierarchical Multi-classifier Framework for Landform Segmentation Using Multi-spectral Satellite Images - A Case Study over the Indian Subcontinent

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Mangai, U.G. ; Dept. of CSE, IIT Madras, Chennai, India ; Samanta, S. ; Das, S. ; Chowdhury, P.R.
more authors

There is an increasing need for automatically segmenting the regions of different landforms from a multispectral satellite image. The problem of Landform classification using data only from a 3-band optical sensor (IRS-series), in the absence of DEM (Digital Elevation Model) data, is complex due to overlapping and confusing spectral reflectance from several different landform classes. We propose a hierarchical method for landform classification for identifying a wide variety of landforms occurring over parts of the Indian subcontinent. At the first stage, the image is classified into one of three broad categories: Desertic, Coastal or Fluvial, using decision fusion of three SVMs (Support Vector Machine). In the second stage, the image is then segmented into different regions of landforms, specifically belonging to the class (category) identified at stage 1. To show the improvement in accuracy of our classification method, the results are compared with two other methods of classification.

Published in:

Image and Video Technology (PSIVT), 2010 Fourth Pacific-Rim Symposium on

Date of Conference:

14-17 Nov. 2010