Improving spatial resolution for CHANG´E-1 imagery using ARSIS concept and Pulse Coupled Neural Networks

To broaden the future application of CHANG´E-1 imagery, including hyperspectral imagery (low spatial resolution of 200m) and CCD imagery (relatively high spatial resolution of 120m), an ARSIS-based method for spatial-spectral fusion is proposed in this paper, which aims at combine high spatial and high spectral resolution. Firstly, ARSIS concept is employed, in which Àtrous wavelet is used to describe images at different resolutions for multiresolution analysis. Secondly, Pulse Coupled Neural Network (PCNN) is employed to search and model a relationship between the high frequencies of the images to be fused for missing information. The ARSIS method preserves the spectral content of the original image for its very definition, and Àtrous wavelet and PCNN prove to be effective means to implement it on CHANG´E-1 Imagery. The experimental results demonstrate that the visual improvement and spectral fidelity of the proposed method outperform many conventional methods of image fusion.