Hyperspectral image classification based on spatial-spectral structure preserving

Hou Banghuan; Yao Minli; Jia Weimin; Shen Xiaowei; Jin wei

doi:10.3788/IRLA201746.1228001

Volume 46 Issue 12

Jan. 2018

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Hou Banghuan, Yao Minli, Jia Weimin, Shen Xiaowei, Jin wei. Hyperspectral image classification based on spatial-spectral structure preserving[J]. Infrared and Laser Engineering, 2017, 46(12): 1228001-1228001(8). doi: 10.3788/IRLA201746.1228001

Citation:

Hou Banghuan, Yao Minli, Jia Weimin, Shen Xiaowei, Jin wei. Hyperspectral image classification based on spatial-spectral structure preserving[J]. Infrared and Laser Engineering, 2017, 46(12): 1228001-1228001(8). doi: 10.3788/IRLA201746.1228001

Hyperspectral image classification based on spatial-spectral structure preserving

doi: 10.3788/IRLA201746.1228001

1.
Department of Information Engineering,Rocket Force Engineering University,Xi'an 710025,China

Received Date: 2017-04-07
Rev Recd Date: 2017-05-12
Publish Date: 2017-12-25

Abstract

Hyperspectral remote sensing image contains the properties of much features(bands) and high redundancy, and the research of hyperspectral image classification focuses on feature selection. To overcome this problem, a hyperspectral image classification algorithm based on spatial and spectral structure preserving was proposed. Considering the physical characteristics of hyperspectral image, the weighted spatial and spectral reconstruction of the image was conducted firstly, in order to incorporate spatial structure information into the spectral feature set automatically, resulting in the spatial-spectral feature set. On the basis that the least square regression model uncovered the global similarity structure and the regularization term revealed the local manifold structure, the intrinsic structure of the spatial-spectral feature set was well preserved by the selected feature subset. The influence of window size and regularization parameter was also analyzed. The experiments on Indian Pines, PaviaU and Salinas datasets show that the classification accuracy of the proposed algorithm reaches 93.22%, 96.01% and 95.90% respectively. The proposed method not only makes full use of the spatial structure information of the hyperspectral image but also uncovers the intrinsic structure of the dataset, which contribute to select more discriminant feature subset and obtain higher classification accuracy compared with conventional methods.
- hypersepctral remote sensing image classification,
- feature selection,
- spatial-spectral features,
- structure preserving

References

[1]	Zhao Huijie, Li Mingkang, Li Na, et al. A band selection method based on improved subspace partition[J]. Infrared and Laser Engineering, 2015, 44(10):3155-3160. (in Chinese)赵慧洁, 李明康, 李娜, 等一种基于改进子空间划分的波段选择方法[J]. 红外与激光工程, 2015, 44(10):3155-3160.
[2]	Deng Chengzhi, Zhang Shaoquan, Wang Shengqian, et al. Hyperspectral unmixing algorithm based on L1 regularization[J]. Infrared and Laser Engineering, 2015, 44(3):1092-1097. (in Chinese)邓承志, 张绍泉, 汪胜前, 等. L1稀疏正则化的高光谱混合像元分解算法比较[J].红外与激光工程, 2015, 44(3):1092-1097.
[3]	Fang Min, Wang Jun, Wang Hongyan, et al. Feature extraction of hyperspectral remote sensing data using supervised neighbor reconstruction analysis[J]. Infrared and Laser Engineering, 2016, 45(10):1028003. (in Chinese)方敏, 王君, 王红艳, 等. 应用监督近邻重构分析的高光谱遥感数据特征提取[J].红外与激光工程, 2016, 45(10):1028003.
[4]	Feng Shuyi, Zhang Ning, Shen Ji, et al. Method of cloud detection with hyperspectral remote sensing image based on the reflective characteristic[J]. Chinese Optics, 2015, 8(2):199-205. (in Chinese)冯书谊, 张宁, 沈霁, 等. 基于反射率特性的高光谱遥感图像云检测方法研究[J]. 中国光学, 2015, 8(2):199-205.
[5]	Liu X W, Wang L, Zhang J, et al. Global and local structure preservation for feature selection[J]. IEEE Transactions on Neural Network and Learning System, 2014, 25(6):1083-1095.
[6]	Nie F P, Huang H, Cai X, et al. Efficient and robust feature selection via joint -norms minimization[C]//Proceedings of Advances in Nerual Information Processing System, 2010:1813-1821.
[7]	Zhu X F, Li X L, Zhang S C, et al. Robust joint graph sparse coding for unsupervised spectral feature selection[J]. IEEE Transactions on Neural Network and Learning System, 2017, 28(6):1263-1275.
[8]	Zhang Q, Tian Y, Yang Y P, et al. Automatic spatial-spectral feature selection for hyperspectral image via discriminative sparse multimodal learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1):261-279.
[9]	Li H C, Xiang S M, Zhong Z S, et al. Multicluster spatial-spectral unsupervised feature selection for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(8):1660-1664.
[10]	Zhou Y C, Peng J T, Chen C L P. Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2):1082-1095.
[11]	Huang Hong, Zheng Xinlei. Hyperspectral image classification with combination of weighted spatial-spectral and KNN[J]. Optics and Precision Engineering, 2016, 24(4):873-880. (in Chinese)黄鸿, 郑新磊. 加权空-谱与最近邻分类器相结合的高光谱图像分类[J]. 光学精密工程, 2016, 24(4):873-880.
[12]	He Fang, Wang Rong, Yu Qiang, et al. Feature extraction of hyperspectral images of weighted spatial and spectral locality preserving Projection[J]. Optics and Precision Engineering, 2017, 25(1):263-273. (in Chinese)何芳, 王榕, 于强, 等. 加权空谱局部保持投影的高光谱图像特征提取[J]. 光学精密工程, 2017, 25(1):263-273.
[13]	Hao Zhicheng, Wu Chuan, Yang Hang, et al. Image detail enhancement method based on multi-scale bilateral texture filter[J]. Chinese Optics, 2016, 9(4):423-431. (in Chinese)郝志成,吴川,杨航,等.基于双边纹理滤波的图像细节增强方法[J].中国光学, 2016, 9(4):423-431.
[14]	He X, Niyogi P. Locality preserving projections[C]//Preceedings of the 17th Annual Conference on Neural Information Processing Systems, 2004, 16:153-160.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Hyperspectral image classification based on spatial-spectral structure preserving

1. Department of Information Engineering,Rocket Force Engineering University,Xi'an 710025,China

Received Date: 2017-04-07

Rev Recd Date: 2017-05-12

Publish Date: 2017-12-25

Key words:

hypersepctral remote sensing image classification /
feature selection /
spatial-spectral features /
structure preserving

Abstract: Hyperspectral remote sensing image contains the properties of much features(bands) and high redundancy, and the research of hyperspectral image classification focuses on feature selection. To overcome this problem, a hyperspectral image classification algorithm based on spatial and spectral structure preserving was proposed. Considering the physical characteristics of hyperspectral image, the weighted spatial and spectral reconstruction of the image was conducted firstly, in order to incorporate spatial structure information into the spectral feature set automatically, resulting in the spatial-spectral feature set. On the basis that the least square regression model uncovered the global similarity structure and the regularization term revealed the local manifold structure, the intrinsic structure of the spatial-spectral feature set was well preserved by the selected feature subset. The influence of window size and regularization parameter was also analyzed. The experiments on Indian Pines, PaviaU and Salinas datasets show that the classification accuracy of the proposed algorithm reaches 93.22%, 96.01% and 95.90% respectively. The proposed method not only makes full use of the spatial structure information of the hyperspectral image but also uncovers the intrinsic structure of the dataset, which contribute to select more discriminant feature subset and obtain higher classification accuracy compared with conventional methods.

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Reference (14)

[1]	Zhao Huijie, Li Mingkang, Li Na, et al. A band selection method based on improved subspace partition[J]. Infrared and Laser Engineering, 2015, 44(10):3155-3160. (in Chinese)赵慧洁, 李明康, 李娜, 等一种基于改进子空间划分的波段选择方法[J]. 红外与激光工程, 2015, 44(10):3155-3160.
[2]	Deng Chengzhi, Zhang Shaoquan, Wang Shengqian, et al. Hyperspectral unmixing algorithm based on L1 regularization[J]. Infrared and Laser Engineering, 2015, 44(3):1092-1097. (in Chinese)邓承志, 张绍泉, 汪胜前, 等. L1稀疏正则化的高光谱混合像元分解算法比较[J].红外与激光工程, 2015, 44(3):1092-1097.
[3]	Fang Min, Wang Jun, Wang Hongyan, et al. Feature extraction of hyperspectral remote sensing data using supervised neighbor reconstruction analysis[J]. Infrared and Laser Engineering, 2016, 45(10):1028003. (in Chinese)方敏, 王君, 王红艳, 等. 应用监督近邻重构分析的高光谱遥感数据特征提取[J].红外与激光工程, 2016, 45(10):1028003.
[4]	Feng Shuyi, Zhang Ning, Shen Ji, et al. Method of cloud detection with hyperspectral remote sensing image based on the reflective characteristic[J]. Chinese Optics, 2015, 8(2):199-205. (in Chinese)冯书谊, 张宁, 沈霁, 等. 基于反射率特性的高光谱遥感图像云检测方法研究[J]. 中国光学, 2015, 8(2):199-205.
[5]	Liu X W, Wang L, Zhang J, et al. Global and local structure preservation for feature selection[J]. IEEE Transactions on Neural Network and Learning System, 2014, 25(6):1083-1095.
[6]	Nie F P, Huang H, Cai X, et al. Efficient and robust feature selection via joint -norms minimization[C]//Proceedings of Advances in Nerual Information Processing System, 2010:1813-1821.
[7]	Zhu X F, Li X L, Zhang S C, et al. Robust joint graph sparse coding for unsupervised spectral feature selection[J]. IEEE Transactions on Neural Network and Learning System, 2017, 28(6):1263-1275.
[8]	Zhang Q, Tian Y, Yang Y P, et al. Automatic spatial-spectral feature selection for hyperspectral image via discriminative sparse multimodal learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1):261-279.
[9]	Li H C, Xiang S M, Zhong Z S, et al. Multicluster spatial-spectral unsupervised feature selection for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(8):1660-1664.
[10]	Zhou Y C, Peng J T, Chen C L P. Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2):1082-1095.
[11]	Huang Hong, Zheng Xinlei. Hyperspectral image classification with combination of weighted spatial-spectral and KNN[J]. Optics and Precision Engineering, 2016, 24(4):873-880. (in Chinese)黄鸿, 郑新磊. 加权空-谱与最近邻分类器相结合的高光谱图像分类[J]. 光学精密工程, 2016, 24(4):873-880.
[12]	He Fang, Wang Rong, Yu Qiang, et al. Feature extraction of hyperspectral images of weighted spatial and spectral locality preserving Projection[J]. Optics and Precision Engineering, 2017, 25(1):263-273. (in Chinese)何芳, 王榕, 于强, 等. 加权空谱局部保持投影的高光谱图像特征提取[J]. 光学精密工程, 2017, 25(1):263-273.
[13]	Hao Zhicheng, Wu Chuan, Yang Hang, et al. Image detail enhancement method based on multi-scale bilateral texture filter[J]. Chinese Optics, 2016, 9(4):423-431. (in Chinese)郝志成,吴川,杨航,等.基于双边纹理滤波的图像细节增强方法[J].中国光学, 2016, 9(4):423-431.
[14]	He X, Niyogi P. Locality preserving projections[C]//Preceedings of the 17th Annual Conference on Neural Information Processing Systems, 2004, 16:153-160.

Hyperspectral image classification based on spatial-spectral structure preserving

doi: 10.3788/IRLA201746.1228001

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References

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