HyperCast: Hyperspectral satellite image broadcasting with band ordering optimization

Abstract This paper presents a novel framework for hyperspectral satellite image broadcasting over wireless channels. We present a new hyperspectral band ordering algorithm that improves the compression performance. The proposed scheme employs the 1D low-complexity Karhunen-Loeve transform (KLT) that uses a clustering approach for spectral decorrelation. After that, the 2D DCT is applied to remove the redundant information from the spatial bands. The DCT components are quantized using a simple DC-quantization algorithm. After that, the transmission power is directly allocated to the quantized data according to their distributions and magnitudes without forward error correction (FEC). These data are transformed by Hadamard matrix and transmitted over a dense constellation. Experiments demonstrate that the proposed scheme improves the average image quality by 6.98 dB and 3.48 dB over LineCast and SoftCast, respectively, and it achieves up to 6.14 dB gain over JPEG2000 with FEC.