|Table of Contents|

Feature Extraction of Hyperspectral Remote Sensing ImageBased on Low Rank and Morphology(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2020年02期
Page:
52-58
Research Field:
测绘科学与技术
Publishing date:

Info

Title:
Feature Extraction of Hyperspectral Remote Sensing ImageBased on Low Rank and Morphology
Author(s):
Jiang Nan1; Zhang Guoming2; Wang Junshu3; Wei Yuchun3
(1.Ecology and Environment Administration for Pearl River Basin and South China Sea,Ministry of Ecology and Environment,Guangzhou 510611,China)(2.Health Statistics and Information Center of Jiangsu Province,Nanjing 210008,China)(3.Key Laboratory for Vir
Keywords:
hyperspectral remote sensing image; morphology; low rank; feature extraction
PACS:
TP751.1
DOI:
10.3969/j.issn.1672-1292.2020.02.008
Abstract:
Hyperspectral remote sensing images with high spectral resolution can describe the reflection spectrum of ground objects in detail,and represent a good ability to classify and identify the ground objects. However,there is usually a high correlation and redundancy among bands,which brings burden to image processing and analysis. Feature extraction and selection of hyperspectral images provide a guarantee for the effective information extraction. In this paper,a feature extraction method,MSEMP,which integrates low rank and morphological profiles is proposed. Low-rank is utilized to simplify the redundant information and obtain the spectral compact expression with the minimum rank of hyperspectral data. Based on low rank representation of hyperspectral image,morphological profiles are extracted by using multi-shaped and multi-scale elements. The proposed algorithm is tested on AVIRIS and ROSIS data,and experimental results show that the classification accuracy based on MSEMP is higher compared with other methods. It indicates that MSEMP is an efficient feature extraction method.

References:

[1] 石茜,杜博,张良培. 一种基于局部判别正切空间排列的高光谱遥感影像降维方法[J]. 测绘学报,2012,41(3):417-420.
[2]孙伟伟. 基于流形学习的高光谱影像降维理论与方法研究[J]. 测绘学报,2014,43(4):439.
[3]SU J Y,YI D W,LIU C J,et al. Dimension reduction aided hyperspectral image classification with a small-sized training dataset:experimental comparisons[J]. Sensors,2017,17(12):2726.
[4]WRIGHT J,GANESH A,RAO S,et al. Robust principal component analysis:exact recovery of corrupted low-rank matrices via convex optimization[C]//Advances in Neural Information Processing Systems. Vancouver,Canada:NIPS200,2009:2080-2088.
[5]LIN B,TAO G,KAI D. Using non-negative matrix factorization with projected gradient for hyperspectral images feature extraction[C]//2013 8th IEEE Conference on Industrial Electronics and Applications(ICIEA). Melbourne,Australia:IEEE,2013:516-519.
[6]施蓓琦,刘春,孙伟伟,等. 应用稀疏非负矩阵分解聚类实现高光谱影像波段的优化选择[J]. 测绘学报,2013,42(3):351-358,366.
[7]CAI J F,CANDèS E J,SHEN Z. A singular value thresholding algorithm for matrix completion[J]. SIAM Journal on Optimization,2010,20(4):1956-1982.
[8]CANDèS E,LI X,MA Y,et al. Robust principal component analysis?[J]. Journal of the ACM,2011,58(3):1-37.
[9]SINGHAL V,AGGARWAL H K,TARIYAL S,et al. Discriminative robust deep dictionary learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2017,55(9):5274-5283.
[10]TOSIC I,FROSSARD P. Dictionary learning[J]. IEEE Signal Processing Magazine,2011,28(2):27-38.
[11]SHEN H,HUANG J Z. Sparse principal component analysis via regularized low rank matrix approximation[J]. Journal of Multivariate Analysis,2008,99(6):1015-1034.
[12]SHEN Y,WEN Z,ZHANG Y. Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization[J]. Optimization Methods and Software,2014,29(2):239-263.
[13]CAI D,HE X,HAN J,et al. Graph regularized nonnegative matrix factorization for data representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(8):1548-1560.
[14]KANG X,LI S,BENEDIKTSSON J. Spectral-spatial hyperspectral image classification with edge-preserving filtering[J]. IEEE Transactions on Geoscience and Remote Sensing,2014,52(5):2666-2677.
[15]REN Y,ZHANG Y,WEI W,et al. A spectral-spatial hyperspectral data classification approach using random forest with label constraints[C]//2014 IEEE Workshop on Electronics,Computer and Applications. Ottawa,Canada:IEEE,2014:344-347.
[16]SUN L,WU Z,LIU J,et al. Supervised spectral-spatial hyperspectral image classification with weighted Markov random fields[J]. IEEE Transactions on Geoscience and Remote Sensing,2015,53(3):1490-1503.
[17]LONG Z,DU Q,YOUNAN N. Hyperspectral feature extraction using contourlet transform[C]//2012 IAPR Workshop on Pattern Recognition in Remote Sensing(PRRS). Tsukuba Science City,Japan:IEEE,2012:1-4.
[18]RASTI B,SVEINSSON J,ULFARSSON M. Total variation based hyperspectral feature extraction[C]//Geoscience & Remote Sensing Symposium. Quebec City,Canada:IEEE,2014:4644-4647.
[19]李亚标,王宝光,李温温. 基于小波变换的图像纹理特征提取方法及其应用[J]. 传感技术学报,2009,22(9):1308-1311.
[20]赵莹,高隽,陈果,等. 一种基于分形理论的多尺度多方向纹理特征提取方法[J]. 仪器仪表学报,2008,29(4):787-791.
[21]POGGI G,SCARPA G,ZERUBIA J B. Supervised segmentation of remote sensing images based on a tree-structured MRF model[J]. IEEE Transactions on Geoscience & Remote Sensing,2005,43(8):1901-1911.
[22]JACKSON Q,LANDGREBE D A. Adaptive bayesian contextual classification based on markov random field[J]. IEEE Transactions on Geoscience & Remote Sensing,2002,40(11):2454-2463.
[23]LI W,CHEN C,SU H,et al. Local binary patterns and extreme learning machine for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience & Remote Sensing,2015,53(7):3681-3693.
[24]SU H,SHENG Y,DU P,et al. Hyperspectral image classification based on volumetric texture and dimensionality reduction[J]. Frontiers of Earth Science,2015,9(2):225-236.
[25]TSAI F,LAI J S. Feature extraction of hyperspectral image cubes using three-dimensional gray-level cooccurrence[J]. IEEE Transactions on Geoscience & Remote Sensing,2013,51(6):3504-3513.
[26]FAUVEL M,BENEDIKTSSON J,CHANUSSOT J,et al. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles[J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(11):3804-3814.
[27]BENEDIKTSSON J,PALMASON J,SVEINSSON J. 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.
[28]BREIMAN L. Random forests[J]. Machine Learning,2001,45(1):5-32.
[29]FRIEDMAN J H. Greedy function approximation:a gradient boosting machine[J]. Annals of Statistics,2001,29(5):1189-1232.

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Last Update: 2020-05-15