|作者：||Dongyu Zhao, Shiping Zhu, Fengchao Wang|
1CRRC Qingdao Sifang Locomotive and Rolling Stock Co., Ltd. Qingdao 266111, China.
2Department of Measurement Control and Information Technology, School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China.
|刊名：||Computers and Electrical Engineering, 2016, Vol.54 , pp.494-505|
|关键词：||Hyperspectral image; Lossy compression; Fractal encoding; Prediction;|
|原始语种摘要：||Abstract(#br)Recently, hyperspectral image compression has become an urgent issue for remote sensing applications . A lossy hyperspectral image compression scheme based on intra-band prediction and inter-band fractal encoding is put forward in this paper. The hyperspectral image is firstly partitioned into several groups of bands (GOBs). Intra-band prediction is applied to the first band in each GOB, exploiting spatial correlation, while inter-band fractal encoding with a local search algorithm is applied to the other bands in each GOB, making use of the local similarity between two adjacent bands. The fractal parameters are signed Exp-Golomb entropy encoded. To improve the decoded quality, the prediction error and fractal residual are further transformed, quantized, and entropy encoded.... Experimental results illustrate that the proposed scheme can obtain a better compression performance with low complexity compared with other well-known methods. In addition, the effect of compression on SVM (Support Vector Machine) classification is presented.|