Integrating Multiple Textural Features for Remote Sensing Image Change Detection

Abstract

This paper proposes a multi-texture change detection method by integrating macro- and micro-texture features. Macro-textures are related to the information defined by the whole image scene, while micro-textures describe distributions and relationships of the gray levels within a local window. Moreover, we propose two strategies, random forests (RF) and a fuzzy set model, to integrate different characteristics of the textures. Experiments were conducted on <small>ZY-3</small> (the first civilian high-resolution stereo mapping satellite of China) orthographic images of the cities of Wuhan and Tokyo, as well as WorldView-2 multi-spectral images of the city of Kuala Lumpur. Results showed that the wavelet-based features obtained the highest accuracy among the macro-textures, while the morphological attributes obtained the best results for the micro-textures. By integrating both micro- and macro-textures, the texture combination using both RF and a fuzzy set model can further improve the accuracy of change detection.