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Segmentation of High Resolution Imagery
B. Krishna Mohan
Centre of Studies in Resources Engineering
Indian Institute of Technology, Bombay
Powai, Mumbai 400076, India
Tel: +91-22-25767684, Fax: +91-22-25723190
Email: bkmohan@iitb.ac.in
S. U. Kadam
Reliance Infocomm Ltd.
Dhirubhai Ambani Knowledge City
Navi Mumbai 400709, India
Tel: +91-22-30373333, Fax: +91 22 2762 4213
E. P. Rao
Department of Civil Engineering
Indian Institute of Technology, Bombay
Powai, Mumbai - 400076, India
Tel: +91-22-25767345, Fax: +91-22-25723480
Introduction
High resolution imagery from resources satellites are revolutionizing the generation of geo-spatial information and building up geographic databases. The amount of detail presented by the 1-metre and sub-metre resolution imagery enables analysis and mapping of the terrain to a level not attempted before. Extraction of information from the high resolution imagery is a challenging task since the per-pixel methods adopted here are likely to fail due to their inability to capture the increased natural variability in the reflectance, and also the fact that each landcover category is comprised of several spatially adjacent pixels. Therefore the methodology to be adopted in such a case has to be region based or object oriented.
Another aspect of information extraction from remotely sensed images (for that matter from any images) is that the objects of interest exhibit tonal / textural structure at different scales. The meaning of scale here is that some objects may contain a large amount of fine detail, while some objects are (intensity-wise) flat with little variation across their spatial extent. Therefore the objects that indeed contain a lot of important detail are studied at one scale, while objects that have only noise induced variations are studied often at a coarser scale. Scale space approach to image analysis was introduced by Marr (1980) and has since then been extensively investigated. One of the most recent examples is the eCognition TM software (Definiens, 2003) wherein the image is segmented based on scale, texture, shape and color (tone).
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