Introduction
Remote sensing is a powerful tool for the regional mapping of natural resources. With the use of imageries during the early stages of development of remote sensing in the mid-seventies, adequate progress has been achieved in the data interpretation. Digital processing of remotely sensed data has gained momentum in the last ten to fifteen years, especially with the availability of digital data. In India, with the establishment of remote sensing agency, attention is focussed on large-scale data processing for natural resource evaluation. One important aspect in remote sensing is the categorisation and classification of spectral measurements taken from satellite sensors into various features on land surface. Recognition of patterns for classification can be carried out if appropriate procedures are adopted. General classification methods have been developed using the image statistics, and their applicability to the processing of data is limited due to the spatial variation of natural resources.
The objective of the study is to describe and compare select image fusion techniques, namely: Band overlay, High Pass Filtering (HPF), Intensity-Hue-Saturation (IHS), Principal Component Analysis (PCA) and IMGFUSE. These techniques are both visual and statistical, in character. This study utilizes a multi-band data from:
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IRS-1C LISS-III (26 February 1998 with 23.5 m spatial resolution)
- IRS-1C panchromatic (26 February 1998 with 5.8 m spatial resolution)
- RADARSAT-1 F1 Ascending (10 July 1999 with 6.25 m spatial resolution)
This is by way of determining the best image fusion technique for land cover mapping for natural resources management.
The present paper is part of a large-scale research, jointly conducted by the University of Madras, India and the University of Waterloo, Canada researchers. The discussion is very brief (Marino, 2001). Only select results are also displayed. The study assesses the utility of multi-band data for the study of land covers which are often difficult to accurately examine with remote sensed data and intended to examine the classification of land cover classes for different band combinations and the potential of the classification methods.
The rationale for classifying the enhanced images generated from the image fusion is two-fold:
First, it is necessary to classify the enhanced images because one of the goals of this study is to produce land-cover map for the area. The idea of classifying the fusion-generated products is based on the assumption that image fusion techniques do provide enhanced images with more information from a broader range of the electromagnetic spectrum than the original data, which can be used to better distinguish features during classification, especially with the combination of optical and radar data (Solberg et al., 1994)
Second, several researchers have used classification to evaluate the success of fusion techniques and to assess the quality of the fusion generated products by comparing classification accuracies to those obtained from the original imagery (for example: Franklin and Blodgett, 1993; Munechika et al, 1993; Haack and Slonecker, 1994; Dwivedi et al., 1997; Sunar and Musaoglu, 1998).