Classification of Land-use Based on Remote Sensing Image Texture Features with Multi-scales and Cardinal Direction Inspired by Domain Knowledge

  • LAN Zeying ,
  • LIU Yang
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  • 1. School of Management, Guangdong University of Technology, Guangzhou 510520, China;
    2. Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China

Received date: 2015-12-22

  Revised date: 2016-07-21

  Online published: 2016-08-31

Supported by

The National Natural Science Foundation of China (No. 41301377)

Abstract

Texture features based on grey level co-occurrence matrix (GLCM) are effective for image analysis, and this paper proposed a new method to construct GLCM with multi-scales and cardinal direction factors inspired by domain knowledge, in order to improve the performance of texture features and solve the uncertainty problems in image classification of land-use. By simulating the process of human visual interpretation, an integrated computation pattern of GIS and RS data were performed. Firstly, on the basis of image registration, some classic GIS spatial data mining algorithms were employed to asymptotically extract domain morphological knowledge; Next, under the responding mechanism derived from correlated analysis, an algorithm for establishing GLCM multi-scale windows that can match categories one by one, an algorithm for determining GLCM weighted cardinal direction windows that can describe observation orientation were designed based on relevant morphology indexes. Experimental results indicate that, there is a strong correlation between domain morphological knowledge and GLCM construction factors, meanwhile, with lower computational complexity, the new method can extract stable texture features to describe actual spatial meanings of complex objects, thereby improve the image classification accuracy of land-use.

Cite this article

LAN Zeying , LIU Yang . Classification of Land-use Based on Remote Sensing Image Texture Features with Multi-scales and Cardinal Direction Inspired by Domain Knowledge[J]. Acta Geodaetica et Cartographica Sinica, 2016 , 45(8) : 973 -982 . DOI: 10.11947/j.AGCS.2016.20150624

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