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Spatial-spectral metric learning for hyperspectral remote sensing image classification
Peng J.2; Zhou Y.1; Chen C.L.P.1
Conference NameConference on Imaging Spectrometry XIX
Source PublicationProceedings of SPIE - The International Society for Optical Engineering
Conference DateAUG 18, 2014
Conference PlaceSan Diego, CA

A spatial-spectral metric learning (SSML) framework for hyperspectral image (HSI) classification is proposed. SSML learns a metric by considering both the spectral characteristics and spatial features represented as the mean of neighboring pixels. It first performs the local pixel neighborhood preserving embedding (LPNPE) to reduce the dimensionality of HSI and meanwhile to preserve the spatial local similarity structure. Then, it learns a spectral and spatial distance metric, separately. Finally, the combination of the spectral and spatial metrics yields a joint spatial-spectral metric. It is followed by a nearest neighbor (NN) classifier for HSI classification. SSML shows good performance over the spectral and spatial NN and SVM on the benchmark hyperspectral data set of Indian Pines.

KeywordClassification Dimension Reduction Hyperspectral Image Metric Learning
URLView the original
Indexed BySCI
WOS Research AreaOptics ; Physics
WOS SubjectOptics ; Physics, Applied
WOS IDWOS:000343913700014
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionFaculty of Science and Technology
Affiliation1.Universidade de Macau
2.Hubei University
Recommended Citation
GB/T 7714
Peng J.,Zhou Y.,Chen C.L.P.. Spatial-spectral metric learning for hyperspectral remote sensing image classification[C],2014.
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