Spatial-spectral constrained graph-based semi-supervised classification for hyperspectral image
HE Hao1,3, SHEN Yonglin1, LIU Xiuguo1, MA Li2
1. Faculty of Information Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China;
2. Faculty of Mechanical and Electronic Information, China University of Geosciences(Wuhan), Wuhan 430074, China;
3. Faculty of Architecture Engineering, Xinjiang University, Urumqi 830047, China
It is difficult to obtain labels of samples for hyperspectral data. Few labeled samples usually lead to low classification accuracy. In view of this situation, an improved spatial and spectral constraint graph-based semi-supervised classification algorithm (SS-GSSC) is proposed. First of all, Euclidean distance combined with radial basis function (RBF) is used to construct the spatial similarity edge weight; Spectral correlation angle (SCA) is used to calculate spectral similarity weights; Then, the two kinds of weights are combined to the form of product to restrict the similarity measurement; Finally, the label propagation algorithm is used to predict the test data labels so as to obtain the classification results. Classification experiments on Indian Pines image and DC Sub image show that, compared with the previous classification algorithm, the algorithm designed by the authors can better eliminate the phenomenon of the existence of the same category map spot included in other categories of scattered points, and can achieve higher classification accuracy under the condition of less label points (25 per class).
[1] Fauvel M,Tarabalka Y,Benediktsson J A,et al.Advances in spectral-spatial classification of hyperspectral images[J].Proceedings of the IEEE,2013,101(3):652-675.
[2] 赵银娣,张良培,李平湘.广义马尔可夫随机场及其在多光谱纹理影像分类中的应用[J].遥感学报,2006,10(1):123-129. Zhao Y D,Zhang L P,Li P X.Universal Markov random fields and its application in multispectral textured image classification[J].Journal of Remote Sensing,2006,10(1):123-129.
[3] 黄昕,张良培,李平湘.融合形状和光谱的高空间分辨率遥感影像分类[J].遥感学报,2007,11(2):193-200. Huang X,Zhang L P,Li P X.Classification of high spatial resolution remotely sensed imagery based on the fusion of spectral and shape features[J].Journal of Remote Sensing,2007,11(2):193-200.
[4] Xia J S,Chanussot J,Du P J,et al.Spectral-spatial classification for hyperspectral data using rotation forests with local feature extraction and Markov random fields[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(5):2532-2546.
[5] Wang L G,Hao S Y,Wang Q M,et al.Semi-supervised classification for hyperspectral imagery based on spatial-spectral label propagation[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,97:123-137.
[6] Ji R R,Gao Y,Hong R C,et al.Spectral-spatial constraint hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(3):1811-1824.
[7] Ghamisi P,Benediktsson J A,Ulfarsson M O.Spectral-spatial classification of hyperspectral images based on hidden Markov random fields[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(5):2565-2574.
[8] Li J Y,Zhang H Y,Huang Y C,et al.Hyperspectral image classification by nonlocal joint collaborative representation with a locally adaptive dictionary[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(6):3707-3719.
[9] Zhu X J.Semi-Supervised Learning with Graphs[D].Pittsburgh,Pennsylvania State:Carnegie Mellon University,2005.
[10] Landgrebe D.Multispectral Data Analysis:A Signal Theory Perspective[R].West Lafayette:School of Electrical and Computer Engineering,Purdue University,1998.
2016, Vol. 28 
