Lossless Compression of Hyperspectral Images Based on Single Neighbor Multi-Bands Prediction
基于单邻点多波段预测的高光谱图像无损压缩算法

Applications for hyperspectral image data are still in their infancy as handling the significant size of the data presents a challenge for the user community. Data compression becomes a key problem. Based on clustering, predicting with single neighbor and self position in multi-bands, and entropy coding, a lossless compression method of hyperspectral images is presented. According to spectral structure, the spectra of a hyperspectral image are clustered by pixels. In every cluster, single spatial neighbor and the same spatial position of the current pixels are used for prediction. Using neighbors in various directions, four predictors are achieved. For each spatial position, one of the predictors is selected to perform the three dimension prediction. The residuals are entropy-coded using the Rice coding. The achieved compression ratios are compared with those of existing methods. The results show that the algorithm is an efficient method.