电子学报2017，Vol.45Issue(10)：2368-2374,7.DOI:10.3969/j.issn.0372-2112.2017.10.009
面向目标检测基于稀疏表示的波段选择方法
Sparse Representation Based Band Selection for Hyperspectral Imagery Target Detection
摘要
Abstract
With the development of hyperspectral imaging technology,the raising spectral resolution improves the ability of target detection and classification.But its great data size and high data dimension also bring challenge to analysis and processing.As a dimensionality reduction technology,band selection (BS) plays an important role in the pre-processing of hyperspectral imagery (HSI).However,few BS algorithms are specially designed for target detection.In this paper,based on analyzing the character of constrained energy minimization (CEM) algorithm,a sparse representation based band selection method (TD-SRBBS) is proposed for HSI target detection.The symmetric Kullback-Leibler divergence is defined for subspatial partition,which makes the original HSI dataset some subset.Sparse reconstruct the detection result in each subset,and then band selection can be implemented based on the one-to-one correspondence between selected bands and nonzero elements of sparse vector.The experiments on real hyperspectral data demonstrate the effectiveness of TD-SRBBS.关键词
波段选择/高光谱图像/稀疏表示/目标检测/子空间划分Key words
band selection/hyperspectral imagery/sparse representation/target detection/subspatial partition分类
信息技术与安全科学引用本文复制引用
唐意东,黄树彩,薛爱军..面向目标检测基于稀疏表示的波段选择方法[J].电子学报,2017,45(10):2368-2374,7.基金项目
国家自然科学基金(No.61273275) （No.61273275）
