Picture of person typing on laptop with programming code visible on the laptop screen

World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.

Explore

Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels

Fang, Leyuan and Li, Shutao and Duan, Wuhui and Ren, Jinchang and Benediktsson, Jon Atli (2015) Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892

[img]
Preview
Text (Fang-etal-IEEE-TOGRS-2015-Classification-of-hyperspectral-images-by-eploiting-spectral-spatial)
Fang_etal_IEEE_TOGRS_2015_Classification_of_hyperspectral_images_by_eploiting_spectral_spatial.pdf - Accepted Author Manuscript

Download (1MB) | Preview

Abstract

For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral-spatial information of superpixels via multiple kernels, termed as superpixel-based classification via multiple kernels (SC-MK). In HSI, each superpixel can be regarded as a shape-adaptive region which consists of a number of spatial-neighboring pixels with very similar spectral characteristics. Firstly, the proposed SC-MK method adopts an over-segmentation algorithm to cluster the HSI into many superpixels. Then, three kernels are separately employed for the utilization of the spectral information as well as spatial information within and among superpixels. Finally, the three kernels are combined together and incorporated into a support vector machines classifier. Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods.