UM
Hyperspectral image classification using distance metric based 1-dimensional manifold embedding
HUI-WU LUO1; YU-LONG WANG1; YUAN YAN TANG1; CHUN-LI LI1; JIAN-ZHONG WANG2
2016-11-02
Conference Name2016 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
Source PublicationProceedings of the 2016 International Conference on Wavelet Analysis and Pattern Recognition
Volume2016-November
Pages247-251
Conference Date10-13 July 2016
Conference PlaceJeju
CountrySouth Korea
PublisherIEEE
Abstract

Hyperspectral remotely sensed image provides very informative information for a wide range of applications that relate to landcover classification. Many studies have shown that the spectral-spatial information is well effective for hyperspectral image (HSI) classification. However, for the spatial based methods, it may sometimes encounter many difficulties in obtaining the spatial prior of different landcovers. Moreover, the spatial prior has to be carefully tuned during each experiment. In this paper, we propose a distance metric learning based 1-dimensional manifold embedding (1DME) for hyperspectral image classification. In our approach, the Mahalanobis matric is first employed to learn an similarity metric of pairwise pixels. The measurement can well indicate proximity of different classes. Then, according to the piecewise affinity, we adopt the developed 1-dimensional manifold embedding to sort the entire data points so that pixels with similar property stay close. Since the entire data points are ordered, several regressors are applied to the ordered sequence, and the averaged results are treated as the prediction. Experiment is conducted on the well acknowledged Indian Pines benchmark data set, and the results validate the efficiency of the proposed method.

KeywordClassification Feature Extraction High Dimensional Data Analysis Manifold Learning Remote Sensing
DOIhttps://doi.org/10.1109/ICWAPR.2016.7731648
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic
WOS IDWOS:000387487900029
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China
2.Department of Mathematics and Statistics, Sam Houston State University, Huntsville, TX 77341, USA
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
HUI-WU LUO,YU-LONG WANG,YUAN YAN TANG,et al. Hyperspectral image classification using distance metric based 1-dimensional manifold embedding[C]:IEEE,2016:247-251.
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