Abstract
Texture features based on the gray-level co-occurrence matrix (GLCM) can effectively improve classification accuracy in geographical analyses of optical remote sensing (RS) images, with the parameters of scale of the GLCM texture window greatly affecting the validity. By analyzing human visual attention characteristics for geo-texture cognition, it was found that there is a strong correlation between the texture scale parameters and the domain shape knowledge in a specified geo-scene. Therefore, a new approach for quickly determining the multi-scale parameters of the texture with the assistance of a geographic information system (GIS) and domain knowledge is proposed in this paper. First, the validity of domain knowledge from an existing GIS database is measured by spatial data mining algorithms, including spatial partitioning, image segmentation, and space-time system evaluation. Second, the general domain shape knowledge of each category is described by the GIS minimum enclosing rectangle indices and rectangular-degree indices. Then, the corresponding multi-scale texture windows can be quickly determined for each category by a correlation analysis with the shape indices. Finally, the Fisher function is used to evaluate the validity of the scale parameters. The experimental results show that the multi-scale value keeps a one-to-one relationship with the classified objects, and their value ranges are from a few to tens, instead of the smaller values of a traditional analysis; thus, effective texture features at such a scale can be built to identify categories in a geo-scene. In this way, the proposed method can increase the total number of categories for a certain geo-scene and reduce the classification uncertainty, as well as better meet the requirements of large-scale image geo-analysis. It also has as high a calculation efficiency and as good a performance as the traditional enumeration method.
Highlights
Texture is an important image spatial feature [1,2,3]
Many texture descriptors have been developed in the past, such as frequency domain analysis based on the Fourier transform, the wavelet transform approach, the hidden Markov approach, the local binary pattern (LBP), the local multiple patterns (LMP), the geostatistical-based approach, the watershed-based approach, the gray-level co-occurrence matrix (GLCM)-based approach, and other statistical approaches [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]
To solve the problems above, this paper proposes a new method for determining multi-scale windows of image GLCM texture descriptors by means of the integrated use of a geographic information system (GIS) and geo-spatial domain knowledge
Summary
Texture is an important image spatial feature [1,2,3]. Many texture descriptors have been developed in the past, such as frequency domain analysis based on the Fourier transform, the wavelet transform approach, the hidden Markov approach, the local binary pattern (LBP), the local multiple patterns (LMP), the geostatistical-based approach, the watershed-based approach, the gray-level co-occurrence matrix (GLCM)-based approach, and other statistical approaches [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. In this paper, a GLCM-based approach that can be associated with geo-knowledge is chosen to build texture features for geo applications This is because the GLCM obtains statistics by using a certain scale window, where one can intuitively observe the actual objects and reflect the domain knowledge (mainly spatial shape attributes). It provides information in image gray direction, interval and change amplitude, so that 14 kinds of texture features can be effectively defined based on it [3], and it meets the requirement for the classification of complex and variable geo-scenes [31]
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