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Land cover mapping: Performance analysis of image-fusion methods
T. Vasantha Kumaran and R. Shyamala
University of Madras, India
Lesley Marino, Phil Howarth and David Wood,
University of Waterloo, Canada
Introduction
Remotely sensed data are used for mapping the extent of land degradation and to generate both enhanced visual colour composite and classified images of land cover for the five desertified villages of the Thevaram Basin. Image fusion techniques are used as a means of combining information from sources such as the Indian Remote Sensing Satellite (IRS 1C) data, both multispectral (Linear Imaging and Self-Scanning sensor (LISS III) and Panchromatic (PAN). The fine beam RADARSAT-1 data is also used since the study involves the use of multi-source remotely sensed data.
Varshney (1997:245) has defined Image Fusion as multisensor data fusion refers to the acquisition, processing and synergetic combination of information of information gathered by various knowledge sources and sensors to provide a better understanding of phenomenon under consideration. Image Fusion is also a form of data fusion which has been defined as a combination of two or more different images to form a new image by using a certain algorithm (Phol and Genderen, 1998). According to Pohl and Genderen (1998) and Varshney (1997), image fusion can be performed at three different processing levels: (a) pixel or data level fusion; (b) feature level fusion, and (c) decision or interpretation level fusion. For the study reported here, the pixel or data level fusion, which is also referred to as data in - data out fusion, has been taken into consideration.
Image fusion is a tool. The main goal of it is to take advantage of the complementary nature of various types of imageries (Chavez, 1986: Vrabel, 1996). It is to produce a new, enhanced composite image that has advantages of both data sets, contains more complete and detailed information (Varshney, 1997), and may enhance interpretation capabilities (Pohl and Genderen, 1998). Image fusion, apart from spatial and spectral enhancement, can be used to detect changes when the combined data are acquired at different times (Smara et al., 1998: Bruzzone et al., 1999: Saraf, 1999). Time factor is included in the data fusion, since it is very difficult to acquire simultaneous multi-sensor data.
The Study Area
The Thevaram Basin is located in an inter-montane valley within the Kambam Valley of Theni district, Tamil Nadu, southern India. It covers an area of approximately 400 km2 between 9° 48¢ N and 10° 2¢ N latitudes and between 77° 13¢ E and 77° 27¢ E longitudes. The basin runs for 30 km in SW-NE direction, bordered by the Theni River in the north, by discontinuous hogback ridges on the east, by the Kombai knolls on the south and by the Western Ghats on the west. The five villages of the study are: Bodi Ammapatti, Maniampatti, Pottipuram, Rasingapuram and Silamalai,, which are located in the northwestern part of the Thevaram Basin. The basin can be divided into three broad physiographic regions: the plains, the uplands, and the hills. The plain lies in the centre of the basin with an elevation of less than 450 m above mean sea level. The upland, which forms the transitional zone between hills and plains, surrounds the plain along the southern and western regions. Eighteen per cent of the basin is occupied by the hills. The Thevaram Basin has semi-arid environment, with a mean annual temperature of 27.2° C and a mean annual relative humidity of 67 per cent. The wind activity has significant effect on climate, vegetation and land use. There is severe wind activity, which has in the last century built a stretch of sand dunes, sands from which are drifting or encroaching upon the agricultural fields. Thus, the winds and the resulting sands cause land degradation. This has caused concern among the people and researchers in the interest of the community.
Fusion Techniques
Image fusion technique is divided into two categories:
- Visual display transforms which involves the colour composition of three bands of imagery displayed in Red-Green-blue(RGB) or other colour transformations such as Intensity-Hue-Saturation (IHS); and
- Statistical or numerical transforms (Harris et al., 1990; Pohl and Genderen, 1998), are based on channel statistics and includes Principal Component Analysis (PCA) Numerical transforms method uses arithmetic operations such as image difference and band ratios.
The most commonly used fusion techniques are band substitution, arithmetic techniques, IHS and PCA. For numerous studies, these techniques have been used but for land use or land cover mapping of arid or semi-arid environment (for example, Lichtenegger et al., 1991; Smara et al., 1998; Saraf, 1999). IHS and PCA are both criticised as having strong spectral distortions in the resulting imagery (Harris et al., 1990;Chavez et al., 1991; Pellemans et al., 1993; Nunez et al., 1999) and are only good for producing images for visual interpretation (Steinnocher, 1997).
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