Lossless Compression of Multispectral Remote Sensing Image Based on Neural Network

Abstract

The algorithm of self-organizing feature mapping neural network is analyzed and improved. A new method based on SOFM codebook design for lossless compression of multispectral image is developed. This method combines vector quantization and classified prediction technique. At first, the multispectral images are transformed to quantization form. Then, residual images are produced and predicted according to classified map. The method removes the intra-band spatial redundancy and the inter-band structural and statistic redundancy, so the better compression results can be obtained. The experimental results by using practical 64-band multispectral images have shown that the lossless compression ratio achieved by the method is not less than 3.2, better than LBG method.