Abstract
Abstract. Resourcesat-1 satellite with its unique capability of simultaneous acquisition of multispectral images at different spatial resolutions (AWiFS, LISS-III and LISS-IV MX / Mono) has immense potential for crop inventory. The present study was carried for selection of suitable LISS-IV MX band for data fusion and its evaluation for delineation different crops in a multi-cropped area. Image fusion techniques namely intensity hue saturation (IHS), principal component analysis (PCA), brovey, high pass filter (HPF) and wavelet methods were used for merging LISS-III and LISS-IV Mono data. The merged products were evaluated visually and through universal image quality index, ERGAS and classification accuracy. The study revealed that red band of LISS-IV MX data was found to be optimal band for merging with LISS-III data in terms of maintaining both spectral and spatial information and thus, closely matching with multispectral LISS-IVMX data. Among the five data fusion techniques, wavelet method was found to be superior in retaining image quality and higher classification accuracy compared to commonly used methods of IHS, PCA and Brovey. The study indicated that LISS-IV data in mono mode with wider swath of 70 km could be exploited in place of 24km LISS-IVMX data by selection of appropriate fusion techniques by acquiring monochromatic data in the red band.
Highlights
Spectral, spatial and temporal resolution information from multi-sensor satellite data can be exploited for mapping and monitoring of natural resources
Utility of multi-sensor data from Resourcesat-1 was evaluated for discrimination of crops using different data fusion techniques for selection of optimal monochromatic band of L-4MX data for merging with L-3 data
Performance of each band of L-4MX data for data fusion indicated that red band is most suitable for data fusion
Summary
Spatial and temporal resolution information from multi-sensor satellite data can be exploited for mapping and monitoring of natural resources. Several studies have been carried out for evaluating data fusion techniques (Wang et al, 2005 and Colditz et al, 2006). The most commonly used data fusion techniques are intensity hue saturation (IHS), principal component analysis (PCA) and Brovey. Several authors have used the multi resolution analysis and wavelet transforms to introduce the spatial information into the spectral bands (Kumar et al, 2000 and Chen Yunhao et al, 2006). Evaluation of these data fusion techniques for merging IRS L-3 and PAN data have been investigated (Mohanty and Majumdar, 2003 and Ray, 2004)
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