压缩感知域高光谱数据高效压缩方法研究

项目名称： 压缩感知域高光谱数据高效压缩方法研究

项目编号： No.61301291

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 王柯俨

作者单位： 西安电子科技大学

项目金额： 25万元

中文摘要： 以压缩感知(CS)为理论基础的高光谱压缩成像技术突破了现有技术对分辨率的限制，在某些应用场合下满足高分辨率应用要求，成为近年来高光谱遥感领域的研究热点之一。但是高光谱压缩成像的数据量仍然很大，不能满足数据传输速率的要求，必须进行压缩。尽管压缩成像数据存在较大冗余，但现有的高光谱图像压缩方法不适用于CS域的压缩成像数据，需要研究新的CS域数据压缩方法。基于此，本项目针对高光谱压缩成像数据的特性，研究基于混合光谱分解的高效压缩方法。通过研究CS域线性混合光谱分解模型及其快速优化求解方法，将高光谱成像数据分解为端元光谱数据和丰度图像数据；通过有效的去相关和上下文建模，对端元光谱数据和丰度图像数据进行无损压缩；通过分析CS域估计误差与重构图像失真和分类精度的相关性，对估计误差进行最优量化。该方法可以获得与传统方式相接近的压缩比，并具有更优的恢复图像质量，同时保持较高的分类精度。

中文关键词： 高光谱数据压缩；压缩感知；混合光谱分解；分类精度；

英文摘要： Hyperspectral compressive imaging (HCI) technology based on the compressive sensing theory is becoming one of the hot research in hyperspectral remote sensing field during the recent years, because it breaks the limit of imaging resolution under the condition of existing technology,and meets the requirements of high-resolution applications in some cases. However the data obtained by HCI remains huge,and it can not meet the speed requirement of data transimission, so that we must compress the HCI data. Alougth the HCI data have much redundance, the existing compression methods for hyperspectral images are not suitable for the HCI data in CS domain, and therefore a novel CS data compression method is necessary. To deal with such problems, we will study on high-effcient HCI data compression based on spectral unmixing in this project, according to the property of HCI data. A linear unmixing model in CS domain and a fast optimization method for solving the model are stdied first, by which the HCI data are decomposed into the endmember spectral data and abundance image data. Then these endmember spectral data and abundance image data will be compressed losslessly through effective decorrelation and context modeling. With the analysis of the correlation between the estimate errors in CS domain and the classification a

英文关键词： hyperspectral data compression；compressive sensing；spectral unmixing；classification accuracy；