Data fusion algorithms for horticulture classification using multi-sensory satellite images

With the advent of numerous remote sensing sensors available for the researcher, the fusion of digital image data has become a imperative tool for classifying remote sensing image and evaluation too. Remote sensing image fusion not only improves the spatial resolution of the original multispectral image, but also improves the spectral quality of merged product. Quantitative and qualitative digital image fusion is an emerging research domain that motivates the scholars for producing high quality image with best multi-spectral capabilities. PAN Sharpened images endow with increased interpretation capabilities as data with various distinctiveness are combined and process effectively. The objective of satellite data fusion is to reduce uncertainty and minimize redundancy in the merged image while maximizing relevant details particular to remote sensing applications. Horticulture in India having great impact on agro based economy. It motivates us for carrying research as very few attempts are made in order to address the issues pertaining with horticulture application of remote sensing. Referring to the results obtained from quantitative and qualitative analysis of fused images, it is obvious that Brovey and Wavelet algorithms outperformed as compared to others.