Feature extraction from remotely sensed data using modified Homomorphic Filtering Approach
Nitin Kumar Tripathi,K V G K Gokhale
Department of Civil Engineering
Indian Institute of Technology
Kanpur - 208016, India
Abstract
It is often seen that due to spectral and spatial constraints as also neighbouring environmental effects, some features or details on Remote Sensing imageries are not so prominent that they can be mapped effectively. Several image processing techniques have been used by various researchers based on features of interest. A digital image processing technique discussed in the present study is very handy as it brings out the various hydro-geomorphological, lithological and landuse / landcover features which otherwise are not distinct on Remote Sensing imagery. As a case study, the processing of the IRS-1B data of Varanasi (India) area in the present work has been undertaken. Use of this approach has enabled delineation of confluence of rivers Ganga and Varuna, not distinct on imagery. In addition, boundaries of sand bars, oxbow lakes, meander patterns, flood-plains, paleochannels, faults, soil types and minor land use / land cover features are demarcated clearly. Some of these features do not appear distinctly even after processing using traditional image processing techniques. The digital images obtained using present methodology are of immense value is preparing a detailed and accurate map of the area for varied applications.
Introduction
In recent years, digital image data have been considerable use in various applications. Digital image, processing techniques generally used area image enhancement, edge detection, feature extraction, segmentation, image coding etc. In the present paper an attempt has been made to evolve a digital image processing approach which can improve the contrast of the satellite imageries to the extent that most of the lithological, hydromorphological and landuse / landcover features which are not so distinct become clearly visible on the output imagery.
In the process of evolution of present methodology various other digital image processing techniques like Robert's Operator, Sobel Operator, high results of these techniques are compared with the current modified homomorphic approach. Homomorphic is a term adopted from the Abstract Algebra to describe a transformation between algebra groups that preserve linear combination (Dubisch4). In general an image of large dynamic range i.e. a natural
scene of earth surface on bright sunny day is recorded on a medium with small dynamic range such as film or photographic paper. This is the cause of significant reduction in mage contrast mainly in brighter and darker regions (Lim1). In order to enhance the image one approach is to reduce its dynamic range and enhance its local contrast before recording it on a medium with small and limited dynamic range (Gonzalez6). The other approach which is followed in present work is to diminish the dynamic range to certain extent, preserve the contrast and introduce a nonlinear component of dynamic range in final output. Several others have earlier used homomorphic approach on Landsat data and according to them "Homomorphic Transforms that permit realistic linear enhancement of Landsat images can lead to a superior product for final interpretation" (Carrol2).
The Study Area
The area selected to test the present image processing algorithm is Varanasi, India. The path and row number in IRS (Indian Remote Sensing Satellite) coverage is 23 - 50. The data is acquired in digital form on a CCT in April 1988. This area offers a scope to test the present methodology in a all respects as it consists of various type of hydrogeomorphological and landuse / landcover features. Ganga and Varuna are the two rivers flowing through the Varanasi urban area.
Methodology
From time to time homomorphology technique has been used by many signal processing and image processing workers for various purposes. Homomorphic filtering (Oppenheim3) is a useful technique for image enhancement when an image is subjected to multiplicative or interference. This technique can be very useful for multispectral Remote Sensing data specially in a scene where vital information contents are lost due to severe cloud effect, radiometric constraints, hue effect etc. These factors influence the dynamic range to such an extent that important details are lost or become indistinct. As already briefed earlier homomorphic approach can be one such approach to accomplish this task.
One simple image model is as follows :
f (n1, n2) - i (n1, n2) r (n1, n2)
where
f (n
1, n
2) represents image
i (n
1, n
2) represents illumination component
r (n
1, n
2) represents reflectance component
