期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 52, 期 6, 页码 3299-3309出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2013.2272378
关键词
Classification; coded aperture; coded-aperture snapshot spectral imaging (CASSI); hyperspectral imagery; principal component analysis (PCA); restricted isometry property (RIP); sparsity
类别
资金
- Army Research Laboratory
- Army Research Office [W911NF-11-1-0368]
- National Science Foundation [EECS-0725422, CCF-0915800]
- Office of Naval Research [NOOO14-1O-C-0199]
Traditional hyperspectral imaging sensors acquire high-dimensional data that are used for the discrimination of objects and features in a scene. Recently, a novel architecture known as the coded-aperture snapshot spectral imaging (CASSI) system has been developed for the acquisition of compressive spectral image data with just a few coded focal plane array measurements. This paper focuses on developing a classification approach with hyperspectral images directly from CASSI compressive measurements, without first reconstructing the full data cube. The proposed classification method uses the compressive measurements to find the sparse vector representation of the test pixel in a given training dictionary. The estimated sparse vector is obtained by solving a sparsity-constrained optimization problem and is then used to directly determine the class of the unknown pixel. The performance of the proposed classifier is improved by taking optimal CASSI compressive measurements obtained when optimal coded apertures are used in the optical system. The set of optimal coded apertures is designed such that the CASSI sensing matrix satisfies a restricted isometry property with high probability. Several simulations illustrate the performance of the proposed classifier using optimal coded apertures and the gain in the classification accuracy obtained over using traditional aperture codes in CASSI.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据