Texture feature fusion with neighborhood oscillating tabu search for high resolution image classification

Abstract

Multi-channel Gabor filters (MGFs) and Markov random fields (MRFs) are two common methods for texture analysis. This paper investigates their integration through a novel algorithm using the neighborhood-oscillating tabu search (NOTS) for high-resolution image classification. The NOTS algorithm fuses the texture features extracted by MGF and MRF. This algorithm has been compared with classical methods such as sequential forward selection, sequential forward floating selection, and oscillating search. Experimental results show that the fused MGF/MRF features have much higher discrimination than pure features, and NOTS outperforms other algorithms with either pure or fused features. The stability and effectiveness of the proposed algorithm have been verified using Brodatz, Ikonos, and QuickBird images. © 2008 American Society for Photogrammetry and Remote Sensing.