Compression of multi-temporal hyperspectral images based on RLS filter

Abstract

The large-scale acquisition of multi-temporal hyperspectral images has increased the demand for a more efficient compression strategy to reduce the large size of such images. In this work, we propose a lossless prediction-based compression technique for multi-temporal images. It removes temporal correlations along with spatial and spectral correlation, reducing the size of time-lapse hyperspectral image significantly. It predicts the pixel value of the target image by a linear combination of pixels from already predicted spectral and temporal bands. The weight matrix used in the prediction is updated using the RLS filter. Experimental results demonstrate the optimal number of bands to be selected for prediction, the comparative strength of individual correlations, and effectiveness of the technique in terms of bit-rate. Our results show that including temporal correlations reduces the bit-rate by 24.07% and our model provides optimization of 18.15% in terms of bits per pixel compared to the state-of-the-art method.