Abstract
Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale can obtain different structure information. To overcome such a drawback also utilizing the structural information, a multiscale superpixel-based sparse representation (MSSR) algorithm for the HSI classification is proposed. Specifically, a modified segmentation strategy of multiscale superpixels is firstly applied on the HSI. Once the superpixels on different scales are obtained, the joint sparse representation classification is used to classify the multiscale superpixels. Furthermore, majority voting is utilized to fuse the labels of different scale superpixels and to obtain the final classification result. Two merits are realized by the MSSR. First, multiscale information fusion can more effectively explore the spatial information of HSI. Second, in the multiscale superpixel segmentation, except for the first scale, the superpixel number on a different scale for different HSI datasets can be adaptively changed based on the spatial complexity of the corresponding HSI. Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed MSSR algorithm over several well-known classifiers.
Highlights
A hyperspectral sensor can capture hundreds of narrow contiguous spectral bands from the visible to infrared spectrum for each image pixel
The effectiveness of the proposed multiscale superpixel-based sparse representation (MSSR) algorithm is tested in the classification of four hyperspectral datasets, i.e., the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)
Compared with the MSSR algorithm, the superpixel-based discriminative sparse model (SBDSM) algorithm is based on the single-scale superpixel and sparse representation
Summary
A hyperspectral sensor can capture hundreds of narrow contiguous spectral bands from the visible to infrared spectrum for each image pixel. For the same region of an image, different structural information can be explored by segmenting superpixels on different scales In view of this reason, multiscale superpixel-based methods are used for feature representation, target detection and recognition in some very recent works [50,51,52]. The superpixel information of different scales is usually integrated via different strategies, such as adopting the similarity between a pixel and the average of pixels within the superpixel [50], converting to a sparse constraint problem [51] and utilizing the convolutional neutral network (CNN) [52] These methods can effectively integrate multiscale information to obtain the optimal result.
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