Abstract
The superpixel-based multiple kernels model uses the average value of all pixels within superpixel as the spatial feature, which results in inaccurate extraction of edge pixels. To solve this problem, a local binary patterns and superpixel-based multiple kernels method is proposed for hyperspectral image (HSI) classification. First, the original HSI is segmented into multiple superpixels by using the entropy rate superpixel segmentation algorithm. On the HSI with superpixel index, the spectral kernel is second obtained by combining the spectral feature map with the radial basis kernel (RBF). By introducing local binary pattern (LBP) and weighted average filtering into RBF, the spatial kernels are obtained within and among superpixels. Finally, the combined kernel containing the abovementioned three kernels is inputted into the support vector machine classifier to generate a classification map. The experimental procedure in this article uses LBP to extract the information in superpixels, which effectively prevents the loss of edge features in superpixels. The experimental results show that the proposed method is superior to the state-of-the-art classifiers for HSI classification.
Highlights
T HE development of remote sensing technology has gone through the stages of panchromatic, color, and multispectral scanning imaging
We propose a classification method of local binary patterns and superpixel-based multiple kernels (SMK) for Hyperspectral image (HSI)
The remainder of this article is organized as follows: Section II presents the superpixel segmentation algorithm, the support vector machine (SVM) and the kernel function; Section III introduces the proposed method; Section IV presents the results of three famous experimental datasets; and Section V provides the conclusion of this article
Summary
T HE development of remote sensing technology has gone through the stages of panchromatic (black and white), color, and multispectral scanning imaging. The Kernel-based methods have gained general acceptance, and these methods [20], [21] use a simple linear weighting scheme to achieve joint learning and the utilization of spatial and spectral information. HUANG et al.: LOCAL BINARY PATTERNS AND SUPERPIXEL-BASED MULTIPLE KERNELS FOR HYPERSPECTRAL IMAGE CLASSIFICATION by applying CKs in SVM. We use weighted average filtering and local binary patterns (LBP) to acquire the spatial characteristics within and among the superpixels, and the obtained spatial kernels among the superpixels, the spatial texture kernels within the superpixel, and the directly extracted spectral kernels were fused together This combination kernel is input into the SVM classifier to generate a classification result map. The remainder of this article is organized as follows: Section II presents the superpixel segmentation algorithm, the SVM and the kernel function; Section III introduces the proposed method (a SMK method); Section IV presents the results of three famous experimental datasets; and Section V provides the conclusion of this article
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
