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


Abstract
High resolution remote sensing is revolutionizing the way earth resources information is extracted from spaceborne sensors (Ikonos, Quickbird, Orbview, TES). Detailed information that was hitherto unavailable such as minor and major roads including widths and surface markings is now possible to derive from remote sensing. In medium resolution images (up to 5 metres resolution), using operators such as line detectors thin features like road networks could be extracted. However, at higher resolutions (1-metre and sub-metre), a road object is really an elongated feature of sizeable width that is far from negligible. Buildings are no longer point objects, but polygons with prominent shadows. In such cases the traditional approaches of image segmentation using per-pixel methods are bound to fail.

There are two key issues to be considered when using high spatial resolution images - we see objects having structure (such as broad, elongated, compact, textured) as well as relationship with other objects (buildings have neighboring road objects). If we reduce the resolution and progressively increase it towards full, we see objects being under-segmented (at coarser resolutions) to over-segmented (at finer resolutions). However, the coarse-to-fine tracking provides global view initially and helps focus on specific details at local level at higher resolutions. This is quite similar to the way human vision system works - from a distance we first mark out broad classes within our view, and then proceed to study the objects of our interest in detail, by going closer to them.

A general region based segmentation / classification system for remote sensing proceeds by initial extraction of regions at multiple levels of resolution. These regions are linked using a number of rules that govern the relationship between the object at a given level with other objects at the same level or other levels. Training of classifiers can now proceed the same way per-pixel classifiers do, with the difference that instead of pixels, each object or region is classified as a separate entity. In this paper, line based, texture based and region based segmentation techniques are applied to 1-metre IKONOS and 5.8 metre IRS -1C panchromatic remotely sensed images and the results are discussed.