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Multiscale Superpixel-Level Subspace-Based Support Vector Machines for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2017.2755061.png)
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摘要
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This letter introduces a new spectral-spatial classification method for hyperspectral images. A multiscale superpixel segmentation is first used to model the distribution of classes based on spatial information. In this context, the original hyperspectral image is integrated with segmentation maps via a feature fusion process in different scales such that the pixel-level data can be represented by multiscale superpixel-level (MSP) data sets. Then, a subspace-based support vector machine (SVMsub) is adopted to obtain the classification maps with multiscale inputs. Finally, the classification result is achieved via a decision fusion process. The resulting method, called MSP-SVMsub, makes use of the spatial and spectral coherences, and contributes to better feature characterization. Experimental results based on two real hyperspectral data sets indicate that the MSP-SVMsub exhibits good performance compared with other related methods.
Keyword:
Hyperspectral image classification
multiscale superpixel segmentation
subspace projection
support vector machines (SVM)
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