Abstract
This paper presents a composite kernel method (MWASCK) based on multiscale weighted adjacent superpixels (ASs) to classify hyperspectral image (HSI). The MWASCK adequately exploits spatial-spectral features of weighted adjacent superpixels to guarantee that more accurate spectral features can be extracted. Firstly, we use a superpixel segmentation algorithm to divide HSI into multiple superpixels. Secondly, the similarities between each target superpixel and its ASs are calculated to construct the spatial features. Finally, a weighted AS-based composite kernel (WASCK) method for HSI classification is proposed. In order to avoid seeking for the optimal superpixel scale and fuse the multiscale spatial features, the MWASCK method uses multiscale weighted superpixel neighbor information. Experiments from two real HSIs indicate that superior performance of the WASCK and MWASCK methods compared with some popular classification methods.
Highlights
Introduction for Hyperspectral Image ClassificationHyperspectral images can be regarded as a collection of corresponding single image obtained in response to different spectral bands
We present a composite kernel based on multiscale weighted adjacent superpixel (MWASCK) method to classify hyperspectral image (HSI)
Several kernel-based classification methods are used to compare with our method: support vector machine with radial basis function (RBF) kernel (SVM-RBF) [6] method, the classic support vector machine with composite kernel (SVMCK) [31] method, the superpixelbased multiple kernels (SCMK) [45] method, the relaxed multiple kernel based on region (RMK) [46] method and the multiscale spatial-spectral kernel based on adjacent superpixel (ASMGSSK) [47]
Summary
Introduction for Hyperspectral Image ClassificationHyperspectral images can be regarded as a collection of corresponding single image obtained in response to different spectral bands. The abundant spectral bands contain a large amount of spectral information, which makes hyperspectral images (HSIs) have a wide range of application prospects [1], such as classification [2], unmixing [3], target detection [4,5], etc. Most approaches only exploit spectral features to classify the HSI without any spatial information, which makes them sensitive to noise and cannot obtain satisfactory results. As demonstrated in [12], hyperspectral data should be viewed as a textured image, not as a few unrelated pixels. For this reason, many HSI classification methods combining spectral and spatial features have been proposed continuously, and the well-pleasing classification results have been obtained. The joint sparse representation [20,21,22,23,24] methods achieve a smoother result by jointly representing the adjacent pixels while representing the target pixels
Sign-in/Register to view highlights & summary 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 callDisclaimer: 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.
