Journal of Jilin University(Earth Science Edition) ›› 2020, Vol. 50 ›› Issue (4): 1249-1260.doi: 10.13278/j.cnki.jjuese.20190133

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Hyperspectral Image Classification Based on Guided Filtering Combined with Local Discrimination Embedding

Zhang Hui1,2, Liu Wanjun2, Lü Huanhuan2   

  1. 1. School of Electronic and Information Engineering, Liaoning Technique University, Huludao 125105, Liaoning, China;
    2. School of Software, Liaoning Technique University, Huludao 125105, Liaoning, China
  • Received:2019-06-28 Published:2020-07-29
  • Supported by:
    Supported by National Natural Science Foundation of China (41871379, 61540056), Key Project of Liaoning Provincial Education Department (LJ2017ZL003) and Liaoning Provincial Natural Science Fund Guidance Plan (20180550450)

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Abstract: Hyperspectral remote sensing image classification is an important part of hyperspectral remote sensing image processing and application. However, hyperspectral remote sensing image has the characteristics of large number of bands and high spatial resolution, which brings some challenges to image classification. To improve the classification accuracy of hyperspectral image and make full use of the spatial and local information, a classification method based on guided filtering combined with local discrimination embedding is proposed. Firstly, the hyperspectral remote sensing image is normalized, the feature extraction is realized by principal component analysis, and the extracted first principal component image is used as the guided image. Secondly, the spatial characteristics of each band are extracted by using the guided filter. Then, the extracted spatial image features are superimposed,and low-dimensional embedding is completed by local Fisher discriminant analysis. Finally, the embedded features are input into SVM to acquire classification results. The experimental results of two hyperspectral images, Indian Pines and Pavia University, show that compared to other relevant methods, the proposed method can obtain higher classification accuracy. When 10% and 100 samples from various ground objects were randomly selected as training samples, the overall classification accuracy increased to 98.28% and 99.45% respectively. At the same time of low dimensional embedding, the proposed method can effectively use the spatial information of images and improve the classification effect.

Key words: hyperspectral image classification, guided filtering, local discrimination embedding, feature extraction, principal component analysis

CLC Number: 

  • TP751
[1] 童庆禧,张兵,张立福.中国高光谱遥感的前沿进展[J]. 遥感学报,2016, 20(5):689-707. Tong Qingxi, Zhang Bing, Zhang Lifu. Current Progress of Hyperspectral Remote Sensing in China[J]. Journal of Remote Sensing, 2016, 20(5):689-707.
[2] Imani M. Manifold Structure Preservative for Hyperspectral Target Detection[J]. Advances in Space Research, 2018, 61(10):2510-2520.
[3] 杜培军,夏俊士,薛朝辉,等. 高光谱遥感影像分类研究进展[J]. 遥感学报,2016, 20(2):236-256. Du Peijun, Xia Junshi, Xue Zhaohui, et al. Review of Hyperspectral Remote Sensing Image Classification[J]. Journal of Remote Sensing, 2016, 20(2):236-256.
[4] Corson M R, Lucke R L, Davis C O, et al. The Hyperspectral Imager for the Coastal Ocean (HICOTM) Environmental Littoral Imaging from the International Space Station[J]. IEEE International Geoscience & Remote Sensing Symposium, 2010, 4(6):3752-3755.
[5] 蒋夕平,吴凤凰,蒋昱,等.基于FastICA算法的高光谱矿物丰度反演高光谱矿物填图应用[J]. 吉林大学学报(地球科学版),2013, 43(5):1681-1686. Jiang Xiping, Wu Fenghuang, Jiang Yu, et al. Hyper-Spectral Mineral Abundance Inversion Based on FastICA Algorithm[J]. Journal of Jilin University (Earth Science Edition), 2013, 43(5):1681-1686.
[6] Xie Weiying, Li Yunsong, Zhou Weiping. Feature Extraction of Hyperspectral Images with a Matting Model[J]. International Journal of Remote Sensing, 2018, 39(5):1510-1527.
[7] 叶涛,韦阿娟,黄志,等. 基于主成分分析法与Bayes判别法组合应用的火山岩岩性定量识别:以渤海海域中生界为例[J]. 吉林大学学报(地球科学版),2019, 49(3):873-880. Ye Tao, Wei Ajuan, Huang Zhi, et al. Quantitative Identification of Volcanic Lithology Based on Comprehensive Principal Component Analysis and Bayes Discriminant Method:A Case Study of Mesozoic in Bohai Bay[J]. Journal of Jilin University (Earth Science Edition), 2019, 49(3):873-880.
[8] Bandos T V, Bruzzone L, Camps-Valls G. Classification of Hyperspectral Images with Regularized Linear Discriminant Analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(3):862-873.
[9] He Xiaofei, Niyogi P. Locality Preserving Projections[M]. Cambridge:MIT Press, 2003.
[10] Sugiyama M. Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis[J]. Journal of Machine Learning Research, 2007, 8(1):1027-1061.
[11] Wei L, Prasad S, Fowler J E, et al. Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4):1185-1198.
[12] 黄鸿,郑新磊. 高光谱影像空-谱协同嵌入的地物分类算法[J]. 测绘学报,2016, 45(8):964-972. Huang Hong, Zheng Xinlei. Hyperspectral Image Land Cover Classification Algorithm Based on Spatial-Spectral Coordination Embedding[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(8):964-972.
[13] 王俊淑,江南,张国明,等. 融合光谱-空间信息的高光谱遥感影像增量分类算法[J]. 测绘学报,2015, 44(9):1003-1013. Wang Junshu, Jiang Nan, Zhang Guoming, et al. Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-Spatial Information[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(9):1003-1013.
[14] Mirzapour F, Ghassemian H. Improving Hyperspectral Image Classification by Combining Spectral, Texture, and Shape Features[J]. International Journal of Remote Sensing, 2015, 36(4):1070-1096.
[15] 叶珍,白璘,粘永健. 基于Gabor特征与局部保护降维的高光谱图像分类算法[J]. 光学学报,2016, 36(10):512-521. Ye Zhen, Bai Lin, Nian Yongjian. Hyperspectral Image Classification Algorithm Based on Gabor Feature and Locality-Preserving Dimensionality Reduction[J]. Acta Optica Sinica, 2016, 36(10):512-521.
[16] 黄鸿,郑新磊,罗甫林. 基于Gabor特征与局部保护降维的高光谱图像分类算法[J]. 光子学报,2016,45(10):152-160. Huang Hong, Zheng Xinlei, Luo Fulin. Hyperspectral Image Classification with Combination of Weighted Mean Filter and Manifold Reconstruction Preserving Embedding[J]. Acta Photonica Sinica, 2016, 45(10):152-160.
[17] He K, Sun J, Tang X. Guided Image Filtering[J]. IEEE Transactions on Software Engineering, 2013, 35(6):1397-1409.
[18] Kumar P S, Supratim G, Sahu P K. Curvelet-Based Multiscale Denoising Using Non-Local Means & Guided Image Filter[J]. IET Image Processing, 2018, 12(6):909-918.
[19] 杨艳春,李娇,党建武,等. 基于引导滤波与改进PCNN的多聚焦图像融合算法[J]. 光学学报,2018, 38(5):86-95. Yang Yanchun, Li Jiao, Dang Jianwu, et al. Multi-Focus Image Fusion Based on Guided Filtering and Improved PCNN[J]. Acta Optica Sinica, 2018, 38(5):86-95.
[20] Pal M, Foody G M. Feature Selection for Classification of Hyperspectral Data by SVM[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(5):2297-2307.
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