Remote sensing image change detection research based on multiple classifiers fusion algorithm with fuzzy integral

Abstract

In this paper, we propose a novel change detection method. Multiple classifiers fusion combine results from various simple changes detection methods to improve change detection accuracy. In detail, we make use of multiple classifiers fusion based on fuzzy integrals for change detection. If the fuzzy measures are well defined, the accuracy of change detection can be improved distinctly. In this paper, we determine the fuzzy measures based on the Genetic Algorithm (GA). Though multiple classifiers fusion has robust performance, the input change detection result is still important. We review proposed pre-classification change detection method, and propose two contextual Fuzzy-C Means (FCM) algorithms and the Self Organization Feature Map (SOFM) change detection method. We select multi-spectral TM and pan SPOT image pairs as test data and apply five different change detection methods. The first experiment shows that different methods will produce different change detection accuracy, and different methods will complement each other. In addition, we apply fuzzy integral aided by genetic algorithm for combining different detection methods. The final experiment shows that our proposed method can improve change detection accuracy and has better performance than single detection method.