@inproceedings{11c65327a4ab4e26acc2ea9aeba3e2b5,
title = "Improving spatial resolution for CHANG'E-1 imagery using ARSIS concept and Pulse Coupled Neural Networks",
abstract = "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 {\`A}trous 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 {\`A}trous 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.",
keywords = "ARSIS, CHANG'E-1, PCNN, hyperspectral image, image fusion",
author = "Bin Zou and Meicun Wang and Junping Zhang and Lamei Zhang and Ye Zhang",
year = "2012",
doi = "10.1109/ICIP.2012.6467312",
language = "英语",
isbn = "9781467325332",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "2125--2128",
booktitle = "2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings",
note = "2012 19th IEEE International Conference on Image Processing, ICIP 2012 ; Conference date: 30-09-2012 Through 03-10-2012",
}