首页 > 论文 > 激光与光电子学进展 > 53卷 > 8期(pp:82801--1)

基于联合稀疏表示与形态特征提取的高光谱图像分类

Hyperspectral Image Classification Based on Joint Sparse Representation and Morphological Feature Extraction

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

为了进一步提高稀疏表示分类能力,提出了基于联合稀疏表示算法与形态学特征的高光谱图像(HSI)分类算法。该算法对高光谱图像提取主成分特征图,并利用结构元素对主成分特征图进行多维的空间结构特征提取,结合提取的形态学特征与原始光谱特征,利用联合稀疏表示算法将同一空间区域中的像元联合进行稀疏系数矩阵的求解,最终通过最小残差判断准则确定像元类别。在AVIRIS与ROSIS HSI上的实验结果表明,该算法在分类效果和分类总精度上都有显著提高。

Abstract

In order to further improve the classification performance of sparse representation classification, a hyperspectral image (HSI) classification algorithm based on joint sparse representation with morphological feature extraction is proposed. To obtain the principle component images, the whole HSI is analyzed by principle component analysis. The closing and opening operations are implemented on principle component images to extract the morphological features. Combining the original spectral and the morphological feature, the pixels in a local region around the central test pixel are simultaneously represented by a set of common atoms of new training dictionary. The classification of HSI is determined by computing the minimum reconstruction error between testing samples and training samples. Experimental results on AVIRIS and ROSIS HSI demonstrate that the effectiveness of the proposed method for improving the classification accuracy and performance.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP751

DOI:10.3788/lop53.082801

所属栏目:遥感与传感器

基金项目:国家自然科学基金(61201422, 61501287, 31300473)、陕西省教育厅科学研究计划项目(14JK1179)

收稿日期:2016-01-27

修改稿日期:2016-03-29

网络出版日期:2016-07-25

作者单位    点击查看

王佳宁:陕西学前师范学院计算机与电子信息系, 陕西 西安 710100

联系人作者:王佳宁(circuitwang@163.com)

备注:王佳宁(1982—),女,博士,讲师,主要从事光学图像处理、模式识别与光学遥感影像的智能化处理等方面的研究。

【1】Wang Yueming, Lang Junwei, Wang Jianyu. Status and prospect of space-borne hyperspectral imaging technology[J]. Laser & Optoelectronics Progress, 2013, 50(1): 010008.
王跃明, 郎均慰, 王建宇. 航天高光谱成像技术研究现状及展望[J]. 激光与光电子学进展, 2013, 50(1): 010008.

【2】Wang Jinnian, Zhang Bing, Liu Jiangui, et al. Hyperspectral data mining-toward target recognition and classification[J]. Journal of Image and Graphics,1999, 4(11): 957-964.
王晋年, 张兵, 刘建贵, 等, 以地物识别和分类为目标的高光谱数据挖掘[J]. 中国图象图形学报, 1999, 4(11): 957-964.

【3】Prasad S, Bruce L M, Chanussot J. Optical remote sensing: Advances in signal processing and exploitation techniques[M]. Heidelberg: Springer, 2011.

【4】Deng Xiaoqin, Zhu Qibing, Huang Min. Variety discrimination for single rice seed by integrating spectral, texture and morphological features based on hyperspectral image[J]. Laser & Optoelectronics Progress, 2015, 52(2): 021001.
邓小琴, 朱启兵, 黄敏. 融合光谱、纹理及形态特征的水稻种子品种高光谱图像单粒鉴别[J]. 激光与光电子学进展, 2015, 52(2): 021001.

【5】Shang Kun, Zhang Xia, Sun Yanli, et al. Sophisticated vegetation classification based on feature band set using hyperspectral image[J]. Spectroscopy and Spectral Analysis, 2015, 35(6): 1669-1676.
尚坤, 张霞, 孙艳丽, 等, 基于植被特征库的高光谱植被精细分类[J]. 光谱学与光谱分析, 2015, 35(6): 1669-1676.

【6】Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1778-1790.

【7】Tarabalka Y, Fauvel M, Chanussot J, et al. SVM-and MRF-based method for accurate classification of hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(4): 736-740.

【8】Fan Liheng, Lü Junwei, Deng Jiangsheng. Classification of hyperspectral remote sensing images based on bands grouping and classification ensembles[J]. Acta Optica Sinica, 2014, 34(9): 0910002.
樊利恒, 吕俊伟, 邓江生. 基于分类器集成的高光谱遥感图像分类方法[J]. 光学学报, 2014, 34(9): 0910002.

【9】Camps-Valls G, Gomez-Chova L, Muoz-Marí J, et al. Composite kernels for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(1): 93-97.

【10】Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.

【11】Chen Y, Nasrabadi N M, Tran T D. Classification for hyperspectral imagery based on sparse representation[C]. Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010: 1-4.

【12】Chen Y, Nasrabadi N M, Tran T D. Hyperspectral image classification using dictionary-based sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3973-3985.

【13】Benediktsson J A, Pesaresi M, Arnaso K. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9): 1940-1949.

【14】Benediktsson J A, Palmason J A, Sveinsson J R. Classification of hyperspectral data from urban areas based on extended morphological profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 480-491.

【15】Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.

【16】Tibshirani R. Regression shrinkage and selection via the LASSO[J]. Journal of the Royal Statistical Society Series B: Methodological, 1996, 58(1): 267-288.

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF