电讯技术2016，Vol.56Issue(7)：717-723,7.DOI:10.3969/j.issn.1001-893x.2016.07.001
基于空谱特性的高光谱图像压缩感知重构
Hyperspectral Image Compressed Sensing Reconstruction Based on Spatial-spectral Characteristics
摘要
Abstract
The existing hyperspectral image compressed sensing reconstruction algorithm can not fully uti-lize the spatial-spectral characteristic of image so that the quality of the reconstructed image is not high e-nough. For this problem,a new compression scheme for hyperspectral images is proposed which is based on variable projection rate sub block compressive sensing and reconstruction of optimized inter spectral predic-tion. At the encoder,all bands of the hyperspectral image is divided into some reference bands and common bands by band clustering,different bands are used to separate the compressed sensing with different preci-sion in order to obtain hyperspectral data. At the decoder, the reference band is reconstructed by using sparsity adaptive matching pursuit(SAMP) algorithm,and for reconstruction of the common band,a new model of optimized inter spectral prediction combined with SAMP algorithm is designed:firstly,the common band is predicted by means of the reconstructed reference band,and it is compressed and projected,then the residual error of the projection value of prediction before and after is calculated for the common band, finally,the SAMP algorithm is used to reconstruct the residual error,which is used to correct the prediction value. Experimental results show that compared with similar algorithms,the proposed algorithm fully consid-ers the spatial-spectral characteristics of hyperspectral images, effectively improves the quality of recon-structed image,and the complexity of encoding is low,and the hardware implementation is easy.关键词
高光谱图像/分块压缩感知/频段聚类/优化谱间预测/稀疏度自适应匹配追踪Key words
hyperspectral image/block compressed sensing/band clustering/optimized inter spectral pre-diction/sparsity adaptive matching pursuit分类
信息技术与安全科学引用本文复制引用
陈善学,胡灿,屈龙瑶..基于空谱特性的高光谱图像压缩感知重构[J].电讯技术,2016,56(7):717-723,7.基金项目
国家自然科学基金青年科学基金资助项目(61302106) （61302106）
河北省自然科学基金资助项目(F2014502029) （F2014502029）
中央高校基本科研业务费专项资金资助项目(2014MS100) （2014MS100）
