Hyperspectral Image Compression and Reconstruction Based on Compressed Sensing

According to the characteristics of hyperspectral images, a novel compression and reconstruction algorithm for hyperspectal images based on compressed sensing is proposed. The random measurements of each image and the linear prediction coefficients are made at the encoder, and then transmitted sequentially to the decoder. At the decoder, in terms of apparent correlations between the adjacent spectral bands, a de-correlation algorithm based on block linear prediction model is used in reconstruction process. The inter-band redundancies are removed from the measurements of current image, thus the de-correlation image data is sparser, which can be reconstructed easily. Experimental results show that the proposed algorithm achieves improved reconstruction performance and efficiently reduces the cost of computation at the encoder, which is more suitable for hardware implementation. © 2015 SERSC.