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Abstract
To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved.
Highlights
Texture is a description of the homogeneity of images using the texel as the fundamental unit, which has a certain scale, regularity and directionality
Texture feature extraction is based on the direction measure and gray level co-occurrence matrix fusion algorithm
The directionality of texture contains a large amount of visual information and image spatial distribution information, which is an effective approach to texture representation and graphical modeling
Summary
Texture is a description of the homogeneity of images using the texel as the fundamental unit, which has a certain scale, regularity and directionality. Texture analysis based on the local spatial variation of intensity or color brightness serves an important role in many applications of remote sensing images [1,2]. Texture analysis is extensively employed in image segmentation, classification, and pattern recognition. Texture feature extraction is an important content of texture analysis, which is an effective method for solving the problems of spectral heterogeneity and complex spatial distribution in the same category [3]. It is very critical to measure the texture reasonably and effectively, because the extracted texture features directly affect the quality of subsequent processing. The methods of texture information extraction are as follows: statistical method, wavelet transform, fractal method, Markov random field (MRF) and so on. Statistical method is simple, easy to implement, and has strong adaptability and robustness.
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