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Land cover mapping: Performance analysis of image-fusion methods


Principal Component Analysis
The PCA is a commonly used tool for image enhancement and the data compression. The original inter-correlated data are mathematically transformed into new, uncorrelated images called components or axes (Chavez and Kwarteng, 1989). The procedure involves a linear transformation so that the original brightness values are re-projected onto a new set of orthogonal axes. PCA is a relevant method for merging remotely sensed imagery because of its ability to reduce the dimensionality of the original data from n to 2 or 3 transformed principal component images, which contain majority of information. For example, PCA can be used to merge several bands of multispectral data (for example, Landsat) with one high spatial resolution band (for example, SPOT Panchromatic). Image fusion can be done in two ways using the PCA: First method is very similar to IHS transformation. Second method involves a forward transformation that is performed on all image channels from the different sensors combined to form one single image file.

IMGFUSE. It is a task within the Image Lock Data Fusion module that is used to enhance the spatial resolution of a low-resolution image using high-resolution image as reference. IMGFUSE task preserves radiometric information on each band individually and maintains statistical integrity. This can be run on geo-coded images or non-geo-coded images. Normally, IMGFUSE is run after non-geo-coded low-and high-resolution images have been "locked" together by running the IMGLOCK task. IMGFUSE can be performed using two images with different spatial resolutions. It can be run separately for both the panchromatic data and the RADARSAT-1 data. KSIZE and MAXGAIN are the other two parameters needed. KSIZE value determines the size of the linear kernels over which the cross correlation modelling is performed (PCI, 1999) on a window size ((2 ´ KSIZE + 1) ´ 3) pixels centred on a pixel. MAXGAIN value controls the sensitivity over relatively flat areas (those areas with similar pixel values, such as water). Higher values increase the sensitivity, while lower values can reduce noise in such regions. A MAXGAIN value that is too low will decrease the amount of detail in the enhanced image.

The Best Solution in Image Fusion
The IHS transformation with the panchromatic stretch produced the best enhanced composite image (Figure 1). All the fusion techniques, with the exception of IMGFUSE, generated composite images with more detailed spatial information than the original multi-spectral data; however, the IHS image is superior. The image provides sharper definition of field boundaries, roads and pathways and is spectrally similar to the original data. The IMGFUSE image has very little spatial detail. The PCA exhibits the most spatial distortion. Based on the comparison of the enhanced composite images generated using the five fusion methods, the best composite image is generated using the IHS technique with the panchromatic stretch, for the whole study area (Figure 2). It is the final classified image for the whole study area.

Conclusion
Based on the comparisons of the enhanced composite images generated using band overlay, the HPF, IHS, PCA and IMGFUSE, the best composite image is generated using IHS technique with the panchromatic stretch. The final spatially enhanced composite image has been presented for perusal and understanding of the nature of fusion. Comparison of the image-fusion techniques in terms of generating a land-cover map revealed that the use of the PCA technique distorted the spectral characteristics of the transformed data set. Results from the IHS technique with the panchromatic stretch and form the IMGFUSE technique were very similar to the band overlay results, which were considered accurate because the characteristics of the data are not altered in any way. In terms of decision to be taken, which technique to apply to generate a land-cover map of the study area, Image Lock Data Fusion (IMGFUSE) technique was rejected.

Acknowledgements
The authors acknowledge with deep gratitude and a sense of thanks the munificent grant from the Shastri Indo-Canadian Institute - Canadian International Development Agency for the research reported here under the Partnership Program Phase II, during 1999-2001.

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