Remote sensing satellites are equipped with different sensors that have varied characteristics. Due to certain constraints, the satellite data can have either high spatial resolution, but low spectral accuracy (panchromatic data) or exhibit high spectral fidelity (multispectral), but acquired with low spatial resolution. Fusion of digital image is an important technique to maximize the advantage of the available multisensor, multitemporal, and multispectral data obtained from remote sensing satellites. Fused images improve the quality of information, in addition to providing superior interpretation potential. Although several fusion methods at the pixel level are available, all these approaches do not consider in a similar manner the small structures to be injected from images of the highest resolution into the lowest resolution. A “preservation trade-off ” exists between the spatial and spectral quality. The main demand of the user concerns the preservation of quality of the multispectral content while increasing the spatial resolution.

This paper aims at the evaluation of spatial quality of fused images using a new approach by which the sharpest points are identified by the Modulus Maxima, and compares with other evaluation parameters. Spectral quality evaluation studies were carried out on fused images using image comparison, correlation, entropies, image noise index, and mutual information, which provide a concise framework for both. These evaluation measures are presented with reference to the IRS data sets. This forms a useful tool in identifying the appropriate technique for an intended application. The paper describes the original methodologies for acquiring unbiased measurements to assess the performance of fused images.Remote sensing satellites revolve around the Earth over a predefined path. They acquire the data within its swath without coming in direct contact with the object, for its designed spatial resolution, spectral resolution, radiometric resolution and temporal resolution.

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