UM
Hyperspectral Image Classification Based on Multiscale Spatial Information Fusion
Li, Hong; Song, Yalong; Chen, C. L. Philip
2017-09
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume55Issue:9Pages:5302-5312
AbstractIn hyperspectral image (HSI) classification, the combination of spectral information and spatial information can be applied to enhance the classification performance. In order to better characterize the variability of spatial features at different scales, we propose a new framework called multiscale spatial information fusion (MSIF). The MSIF consists of three parts: multiscale spatial information extraction, local 1-D embedding (L1-DE), and information fusion. First, spatial filter with different scales is used to extract multiscale spatial information. Then, L1-DE is utilized to map the spectral information and spatial information at different scales into 1-D space, respectively. Finally, the obtained 1-D coordinates are used to label the unlabeled spatial neighbors of the labeled samples. The proposed MSIF captures intrinsic spatial information contained in homogeneous regions of different sizes by multiscale strategy. Since the spatial information at different scales is processed separately in MSIF, the variance of spatial information at different scales can be reflected. The use of L1-DE reduces computational cost by mapping high-dimensional samples into 1-D space. In MSIF, the L1-DE and information fusion are used iteratively, and the iterative process terminates in a finite number of steps. The algorithm analysis demonstrates the effectiveness of the proposed method. The experimental results on four widely used HSI data sets show that the proposed method achieved higher classification accuracies compared with other state-of-the-art spectral-spatial classification methods.
KeywordHyperspectral image (HSI) classification local 1-D embedding (L1-DE) multiscale spatial information
DOI10.1109/TGRS.2017.2705176
URLView the original
Indexed BySCIE
Language英语
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000408346600038
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to ArticleWOS
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Cited Times [WOS]:16   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Li, Hong,Song, Yalong,Chen, C. L. Philip. Hyperspectral Image Classification Based on Multiscale Spatial Information Fusion[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2017,55(9):5302-5312.
APA Li, Hong,Song, Yalong,&Chen, C. L. Philip.(2017).Hyperspectral Image Classification Based on Multiscale Spatial Information Fusion.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,55(9),5302-5312.
MLA Li, Hong,et al."Hyperspectral Image Classification Based on Multiscale Spatial Information Fusion".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 55.9(2017):5302-5312.
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