基于边缘修正的高光谱图像超像素空谱核分类方法

doi:10.3969/j.issn.0372-2112.2019.01.010

电子学报 ›› 2019, Vol. 47 ›› Issue (1): 73-81.DOI: 10.3969/j.issn.0372-2112.2019.01.010

基于边缘修正的高光谱图像超像素空谱核分类方法

陈允杰¹, 马辰阳¹, 孙乐², 詹天明³

1. 南京信息工程大学数学与统计学院, 江苏南京 210044;
2. 南京信息工程大学计算机与软件学院, 江苏南京 210044;
3. 南京审计大学信息与工程学院, 江苏南京 211815

收稿日期:2017-09-09 修回日期:2018-03-22 出版日期:2019-01-25
- 通讯作者:
- 孙乐
- 作者简介:
- 陈允杰 男,1980年生于江苏盐城.南京信息工程大学数学与统计学院教授,研究方向为数值计算、模式识别、图像处理.E-mail:priestcyj@nuist.edu.cn;马辰阳 男,1995年生于山西运城.南京信息工程大学数学与统计学院硕士研究生,研究方向为高光谱图像分类.E-mail:15189826258@163.com;詹天明 男,1984年10月生于江苏高邮,南京审计大学信息与工程学院副教授,研究方向为模式识别、图像处理与大数据分析.E-mail:ztm@nau.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61672291，No.61601236，No.61502206）; 江苏省自然科学 (No.BK20150923，No.BK20150523）

Edge-Modified Superpixel Based Spectral-Spatial Kernel Method for Hyperspectral Image Classification

CHEN Yun-jie¹, MA Chen-yang¹, SUN Le², ZHAN Tian-ming³

1. College of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China;
2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China;
3. School of Information and Engineering, Nanjing Audit University, Nanjing, Jiangsu 211815, China

Received:2017-09-09 Revised:2018-03-22 Online:2019-01-25 Published:2019-01-25

摘要/Abstract

摘要： 本文提出了一种边缘修正的超像素空间光谱核分类方法，该方法能够有效解决构建空谱核时超像素方法提取的空间信息完全依赖于同一个超像素特征，边缘处像素空间信息刻画不准确这一缺陷，从而有效提升分类精度.首先本文提出一种固定窗口与超像素结合的同质区域选择方法，对提取的邻域像素进行赋权，将超像素中固定窗口外的像素权值置零，得到修正的空间光谱核；其次，进一步考虑相邻超像素之间的相关性，得到相邻超像素间的空间特征光谱核，并与上一步中的空间光谱核进行凸组合得到修正的超像素空间光谱核，最后采用支持向量机进行分类.真实高光谱数据实验结果表明：本文方法能有效克服超像素空谱核的空间信息不稳定性，分类精度优于现有的最新的分类方法.

关键词: 高光谱图像分类, 空谱核, 超像素核, 核方法

Abstract: In order to alleviate the drawback that the spatial information of any pixel in a superpixel for generating the spatial-spectral kernel is totally determined by the same biased superpixel feature,especially for spatial information of the pixels located at the boundary,we propose an edge-modified superpixel based spatial-spectral kernel method for hyperspectral classification.On one hand,we combine the fixed window and superpixel to determine the homogeneous regions in a weighting strategy,in which the weights for pixels outside the fixed window are set to zero.Then we obtain the modified spectral-spatial kernel based on the weighted homogeneous regions.On the other hand,by considering the correlation among adjacent superpixels,we extract the spatial features among those superpixels to generate the inter-superpixel based spectral-spatial kernel.Finally,we combine the two spatial-spectral kernels in a convex way and employ support vector machine (SVM) for classification.Experimental results on two real hyperspectral data sets indicate that the proposed method could overcome the instability caused by superpixel-based spatial information extraction technique,and lead to better classification results than other state-of-the-art classifiers.

Key words: hyperspectral classification, spatial-spectral kernel, superpixel based kernel, kernel-based method

中图分类号:

TP751

陈允杰, 马辰阳, 孙乐, 詹天明. 基于边缘修正的高光谱图像超像素空谱核分类方法[J]. 电子学报, 2019, 47(1): 73-81.

CHEN Yun-jie, MA Chen-yang, SUN Le, ZHAN Tian-ming. Edge-Modified Superpixel Based Spectral-Spatial Kernel Method for Hyperspectral Image Classification[J]. Acta Electronica Sinica, 2019, 47(1): 73-81.

参考文献

[1] PATEL N K,PATNAIK C,DUTTA S,et al.Study of crop growth parameters using airborne imaging spectrometer data[J].International Journal of Remote Sensing,2001,22(12):2401-2411.
[2] LI J,BIOUCAS-DIAS J M,Plaza A.Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields[J].IEEE Transactions on Geoscience & Remote Sensing,2012,50(3):809-823.
[3] HUANG C,DAVIS L S,TOWNSHEND J R G.An assessment of support vector machines for land cover classification[J].International Journal of Remote Sensing,2002,23(4):725-749.
[4] JIA X,Richards J.Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification[J].IEEE Transactions on Geoscience & Remote Sensing,1999,37(1):538-542.
[5] SUMARSONO A,DU Q.Low-rank subspace representation for supervised and unsupervised classification of hyperspectral imagery[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2016,9(9):4188-4195.
[6] CAMPS-VALLS G,BRUZZONE L.Kernel-based methods for hyperspectral image classification[J].IEEE Transactions on Geoscience & Remote Sensing,2005,43(6):1351-1362.
[7] CHEN Y,NASRABADI N M,TRAN T D.Hyperspectral image classification via kernel sparse representation[J].IEEE Transactions on Geoscience & Remote Sensing,2013,51(1):217-231.
[8] MELGANI F,BRUZZONE L.Classification of hyperspectral remote sensing images with support vector machines[J].IEEE Transactions on Geoscience & Remote Sensing,2004,42(8):1778-1790.
[9] 孙乐,吴泽彬,冯灿,等.一种新的两分类器融合的空谱联合高光谱分类方法[J].电子学报,2015,43(11):2210-2217. SUN L,WU Z B,FENG C,et al.A novel two-classifier fusion method for spectral-spatial hyperspectral classification[J].Acta Electronica Sinica,2015,43(11):2210-2217.(in Chinese)
[10] MOSER G,SERPICO S B.Combining support vector machines and Markov random fields in an integrated framework for contextual image classification[J].IEEE Transactions on Geoscience & Remote Sensing,2013,51(5):2734-2752.
[11] CHEN Y,NASRABADI N M,Tran T D.Hyperspectral image classification using dictionary-based sparse representation[J].IEEE Transactions on Geoscience & Remote Sensing,2011,49(10):3973-3985.
[12] SUN L,WU Z,LIU J,XIAO L,WEI Z.Supervised spectral-spatial hyperspectral image classification with weighted Markov random fields[J].IEEE Transactions on Geoscience & Remote Sensing,2015,53(3):1490-1503.
[13] 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 & Remote Sensing,2005,43(3):480-491.
[14] CAMPS-VALLS G,GOMEZ-CHOVA L,MUNOZ-MARI J,et al.Composite kernels for hyperspectral image classification[J].IEEE Geoscience & Remote Sensing Letters,2006,3(1):93-97.
[15] LIU J,WU Z,WEI Z,XIAO L,SUN L.Spatial-spectral kernel sparse representation for hyperspectral image classification[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2013,6(6):2462-2471.
[16] DUAN W,LI S,FANG L.Superpixel-based composite kernel for hyperspectral image classification[A].IEEE International Conference on Geoscience and Remote Sensing Symposium[C].IEEE,2015.1698-1701.
[17] SUN Z,WANG C,WANG H,et al.Learn multiple-kernel SVMs for domain adaptation in hyperspectral data[J].IEEE Geoscience & Remote Sensing Letters,2013,10(5):1224-1228.
[18] FANG L,LI S,DUAN W,et al.Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels[J].IEEE Transactions on Geoscience & Remote Sensing,2015,53(12):6663-6674.
[19] COVER T M.Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition[J].IEEE Transactions on Electronic Computers,1965,EC-14(3):326-334.
[20] JOLLIFFE,Ian.Principal Component Analysis[M].Springer-Verlag,1986.
[21] LIU M Y,TUZEL O,RAMALINGAM S,et al.Entropy rate superpixel segmentation[A].IEEE Computer Vision and Pattern Recognition[C].IEEE,2011.2097-2104.
[22] CHANG C C,LIN C J.LIBSVM:A library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.

基于边缘修正的高光谱图像超像素空谱核分类方法

Edge-Modified Superpixel Based Spectral-Spatial Kernel Method for Hyperspectral Image Classification

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

[1]	谢晓丹, 李伯虎, 柴旭东. 计算和存储空间受限下的数据稀疏核分析方法[J]. 电子学报, 2017, 45(6): 1362-1366.
[2]	余晓东, 雷英杰, 宋亚飞, 岳韶华, 申晓勇. 基于核距离的直觉模糊c均值聚类算法[J]. 电子学报, 2016, 44(10): 2530-2534.
[3]	陈思宝, 许立仙, 罗斌. 基于多重核的稀疏表示分类[J]. 电子学报, 2014, 42(9): 1807-1811.
[4]	王莹, 李文辉, 傅博, 林逸峰, 倪洪印. 核二维子类鉴别分析[J]. 电子学报, 2013, 41(5): 992-996.
[5]	殷俊, 杨万扣. 核稀疏保持投影及生物特征识别应用[J]. 电子学报, 2013, 41(4): 639-645.
[6]	王峰;张鸿宾. 最优双核复合分类算法的构造[J]. 电子学报, 2012, 40(2): 260-265.
[7]	赵建邦;高琳;宋佳. 一种基于代谢路径构建系统发生树的有效方法[J]. 电子学报, 2009, 37(8): 1633-1638.
[8]	潘志松;陈斌;缪志敏;倪桂强. One-Class分类器研究[J]. 电子学报, 2009, 37(11): 2496-2503.
[9]	尹传环, 田盛丰, 牟少敏. 一种面向间隙核函数的快速算法[J]. 电子学报, 2007, 35(5): 875-881.
[10]	庞彦伟, 俞能海, 沈道义, 刘政凯. 基于核邻域保持投影的人脸识别[J]. 电子学报, 2006, 34(8): 1542-1544.
[11]	潘志松;陈松灿;张道强. 原空间中的核SOM分类器[J]. 电子学报, 2004, 32(2): 227-231.