Artificial neural network classification of wetland integrating GIS data: A case study of Dongtan wetland in Chongming, Shanghai
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摘要: 以上海崇明岛东滩湿地为例，利用改进的BP算法结合主成分分析，将光谱信息的主成分、NDVI、MNDWI以及GIS数据作为神经网络的输入参数对东滩湿地进行神经网络分类.结果表明，神经网络分类能够有效的提高分类的精度，适合湿地分类.Abstract: This paper took Dongtan wetland in Chongming Island, Shanghai, as a case study; using the PCA outputs of TM surface feature spectrum, NDVI, MNDWI, DEM and the GIS data as inputting elements of an Artificial Neural Network (ANN), combined with improved BP algorithm, an ANN classification was applied to the Dongtan wetland. The results show that the ANN classification method improves the classification accuracy, and can effectively distinguish those objects with similar TM spetra.
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Key words:
- wetlandremote sensing classificationANN /
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