A Fusion Approach of Multi-Sensor Remote Sensing Data Based on Retina Model

The information content of a single image is mainly limited by the spatial and spectral resolution of the imaging system. Current imaging systems somehow offer a trade-off between high spatial and high spectral resolution. In order to obtain the both characteristic in a single image, that is high spatial and spectral resolution, a technique image fusion can be employed. This paper presents a multi-resolution data fusion scheme, based on visual channels image decomposition. Motivated by analytical results obtained from retina based image analysis, or image decomposition into spatial frequencies incorporates the visual channels phenomena, the energy packing the spectral features (colors) are distributed in the lower frequency subbands, and the spatial features, edges, are distributed in the higher frequency sub-bands. Comparing with HIS, PCA, Brovey transform and wavelet transform methods; the best spectral and spatial quality is only achieved simultaneously with the proposed feature based data fusion. In this method, there is no need to resample images, which is an advantage over HIS, PCA, Brovey and wavelet methods, it can perform in any aspect ratio between the panchromatic image and multi-spectral images pixels.

Keywords:

Multi-sensor Data Fusion , image fusion , remote sensing , retina model