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Lossless compression of multispectral satellite images

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3 Author(s)
Chai Kiat Yeo ; School of Electrical and Electronics Engineering, Nanyang Technological University, Nanyang Avenue, S 639798, Singapore ; Ing Yann Soon ; Chiew Tong Lau

This paper describes a neural network-based technique to compress multispectral SPOT satellite images losslessly. The technique harnesses the pattern recognition property of one-hidden-layer back propagation neural networks to exploit both the spatial and the spectral redundancy of the three-band SPOT images. The networks are initially trained on samples of the SPOT images with a unique network for each of the bands. The resultant trained nonlinear predictors are then used to predict the target SPOT images. Predicted errors are entropy-coded using multi-symbol arithmetic coding. This technique achieves compression ratios of 2.1 times and 3.2 times for urban and rural SPOT images respectively which are above 10% better than using lossless JPEG compression techniques. In comparison with JPEG2000 lossless compression, the proposed technique is 5% better.

Published in:

Journal of Communications and Networks  (Volume:1 ,  Issue: 4 )