Compression of Hyperspectral Imagery Based on Compressive Sensing and Interband Prediction

摘要

An efficient compression algorithm for hyperspectral imagery based on compressive sensing and interband linear prediction is proposed which has the advantages of high compression performance and low computational complexity by exploiting the strong spectral correlation. At the encoder, the random measurements of each frame are made, quantized and transmitted to the decoder independently. The prediction parameters between adjacent bands are also estimated using the linear prediction algorithm and transmitted to the decoder. At the decoder, a new reconstruction algorithm with the proposed initialization and stopping criterion is employed to reconstruct the current frames with the assistance of the prediction frame, which is derived from the previous reconstructed neighboring frames and the received prediction parameters using the same prediction algorithm. Experimental results show that the proposed algorithm not only obtains about 1.1 dB gains but greatly decreases decoding complexity. Furthermore, our algorithm has the characteristics of low-complexity encoding and facility in hardware implementation.