Abstract. A new means to distinguish the habitat spectacles of the representative marshes in China based on artificial intelligence is presented in this article. Three typical instances including Yancheng mudflat marsh, Zoige plateau marsh, and Dongzhai Harbor mangrove forest marsh were investigated. Firstly, the RGB true-color pictures of the marsh habitat spectacles were resized to the appropriate sizes and switched to gray intensity pictures. Secondly, the GIST descriptors were evaluated for encoding the marsh habitat spectacles at both a basic level and a superordinate level. Thirdly, the principal component analysis algorithm was performed to extract the principal components from the encoded features. Finally, the multi-class support vector machine (MSVM) algorithm was used to discriminate the marsh habitat spectacles using the principal components. The recognition percisions for the training and test set reached 72.5% and 70.6%, respectively. It was accounted that the proposed methods could be applied to distinguishing the representative marsh habitat spectacles in China. Keywords: Classification, GIST descriptiors, Marsh Habitat spectacle, Multi-class support vector machine, Principal component analysis.
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