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Land cover mapping: Performance analysis of image-fusion methods
Applications
Remotely sensed data have been successfully used for a variety of applications in arid and semi-arid regions around the world. The includes the detection and monitoring of land-use change in Rajasthan, India (Ram and Kolarkar, 1993; Kumar et al., 1993) and wasteland mapping to identify potential areas of afforestation in Hisar district of Haryana state, India (Shedha et al., 1996). The two most important land cover types in arid environments are sand, or sand features, and vegetation. In the study conducted by Kumar et al.(1993) Landsat MSS data from 1973 and 1986 were used to determine land cover change and monitoring desertification in the Thar Desert, India in which it was concluded that the MSS data were useful for recognizing and mapping different types of dunes and the substantial change in their boundaries.
Pre-Processing
Image rectification and restoration procedures are often termed as preprocessing operations because they normally precede manipulation and analysis of the digital image data in order to extract specific information. Manipulation and interpretation of digital images with the aid of computer forms the component of digital image processing. In order to correct Image data of its distortion or degradation due to image acquisition process, image rectification and restoration are used. Preprocessing involves the correction of both systematic (for example, scan skews, mirror-scan velocity, panoramic distortion, platform velocity, earth rotation and perspective) and non-systematic distortions (for example, altitude and attitude). Non-systematic errors are corrected by performing both image-to-map geometric rectification and image-to-image registration. Geometric correction is usually a two-step process involving polynomial transformation and image re-sampling.
Image Fusion Methods
For the purpose of study, a comparison of five image fusion techniques in terms of their effectiveness for merging both the IRS-1C LISS-III and panchromatic data and the LISS-III and RADARSAT-1 data has been attempted. The major fusion techniques that are applied, but not fully described, include band overlay, high-pass filtering (HPF), intensity-hue-saturation (IHS) transformation, and principal component analysis (PCA), and one new technique, PCI EASI / PACE's IMGFUSE.
Band Overlay
The band substitution is the simplest image fusion technique (Franklin and Blodgett, 1993; Pohl and Genderen, 1993; Vrabel, 1996; Pohl and Genderen, 1998). It has been used for various applications such as agricultural crop classification, land use mapping, vegetation assessment and monitoring (Marino, 2001). The major advantage of this technique is that there are no changes to the radiometric qualities of the data since there is no radiometric enhancement of the data. This technique is most often used when the two sources are highly correlated. Panchromatic sharpening involves the substitution of the panchromatic band for the multi-spectral band covering the same region as the panchromatic band (Jensen, 1996). The generation of colour composite images is limited to the display of only three bands corresponding to the colour guns of the display device (red-green-blue). As the panchromatic band has a spectral range covering both the green and red channels (PAN 0.50-0.75 mm; green 0.52-0.59 mm; red 0.62-0.68 mm), the panchromatic band could be used as a substitute for either of those bands.
High-Pass Filtering Method
The HPF fusion method is a specific application of arithmetic techniques used to fuse imagery, which involves use of arithmetic operations such as addition, subtraction, multiplication and ratioing (Vrabel, 1996). HPF is an arithmetic technique that applies a spatial enhancement filter to the high-resolution image before the two data sets are merged together on a pixel-by-pixel basis. The HPF fusion combines both spatial and spectral information using the band-addition approach. Chavez et al. (1991) found that when compared to the IHS and PCA, the HPF method exhibits less distortion in the spectral characteristics of the data; and distortions were minimal and difficult to detect. This conclusion was based on statistical, visual and graphical analysis of the spectral characteristics of the data.
Intensity-Hue-Saturation
IHS transformation is one of the most widely used methods for merging complementary, multi-sensor data sets (Chavez et al., 1991; Pellemans et al., 1993; Vrabel, 1996). The IHS transform provides an effective alternative to describing colours by the red-green-blue display co-ordinate system. The possible range of digital numbers (DNs) for each colour component is 0 to 255 for 8-bit data. Each pixel is represented by a three-dimensional coordinate position within the colour cube. Pixels having equal components of red, green and blue lie on the grey line, a line from the cube to the opposite corner (Lillesand and Kiefer, 2000).
The IHS transform is defined by three separate and orthogonal attributes, namely intensity, hue, and saturation (Harris et al., 1990). Intensity represents the total energy or brightness in an image and defines the vertical axis of the cylinder. Hue is the dominant or average wavelength of the colour inputs and defines the circumferential angle of the cylinder. It ranges from blue (0 / 360°) through green, yellow, red, purple, and then back to blue (360 / 0°). Saturation is the purity of a colour or the amount of white light in the image and defines the radius of the cylinder (Harris et al., 1990). (Chavez et al., 1991; Pellemans et al., 1993;) cautioned that of all methods to merge multi-spectral data, the IHS method distorts spectral characteristics the most and should be used with caution if detailed radiometric analysis is to be performed. Although IRS 1C LISS III acquires data in four bands, only three bands are used for the study neglecting the fourth due to the poor spatial resolution. IHS transform is more successful in panchromatic sharpening with true colour composites than when the colour composites include near or mid-infrared bands.
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