Abstract: In order to improve the change detection accuracy of water surrounding environment, an improved change detection method was proposed. This method was based on the combination of spectral and textural features, and fused the index feature to construct a hybrid feature space. The simple linear iterative cluster(SLIC)algorithm was used to obtain ground objects by processing the superimposed images. Meanwhile, the proposed method synthesized various forward-backward heterogeneity information to construct the forward-backward heterogeneity of ground objects. The EM algorithm and the minimum error Bayes decision theory were used to obtain the change information of the images on two phases. By eliminating the pseudo change information of vegetation, the relative robust and exact detection results could be achieved. Experimental results showed that the proposed method could effectively distinguish the useful change information from uninterested disturbance information and pseudo change information, and had low false detection ratio and low missing detection ratio. The accuracy of the proposed method could reach more than 96%. Moreover, this method could intelligently recognize the abnormal land-use changes around lakes and reservoirs.

Key words: water body, simple linear iterative cluster, hybrid feature space, forward-backward heterogenicity, change detection