A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation

• Published in: Remote sensing (Basel, Switzerland) Vol. 6; no. 9; pp. 8424 - 8445
• Main Authors: Huang, Xin, Liu, Xiaobo, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2014
• Subjects: classification; clustering; gray level co-occurrence matrix; hyperspectral; multispectral; Remote sensing; sparse representation; texture
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs6098424

This study proposes a novel method for multichannel image gray level co-occurrence matrix (GLCM) texture representation. It is well known that the standard procedure for the automatic extraction of GLCM textures is based on a mono-spectral image. In real applications, however, the GLCM texture feature extraction always refers to multi/hyperspectral images. The widely used strategy to deal with this issue is to calculate the GLCM from the first principal component or the panchromatic band, which do not include all the useful information. Accordingly, in this study, we propose to represent the multichannel textures for multi/hyperspectral imagery by the use of: (1) clustering algorithms; and (2) sparse representation, respectively. In this way, the multi/hyperspectral images can be described using a series of quantized codes or dictionaries, which are more suitable for multichannel texture representation than the traditional methods. Specifically, K-means and fuzzy c-means methods are adopted to generate the codes of an image from the clustering point of view, while a sparse dictionary learning method based on two coding rules is proposed to produce the texture primitives. The proposed multichannel GLCM textural extraction methods were evaluated with four multi/hyperspectral datasets: GeoEye-1 and QuickBird multispectral images of the city of Wuhan, the well-known AVIRIS hyperspectral dataset from the Indian Pines test site, and the HYDICE airborne hyperspectral dataset from the Washington DC Mall. The results show that both the clustering-based and sparsity-based GLCM textures outperform the traditional method (extraction based on the first principal component) in terms of classification accuracies in all the experiments.