Mangrove forest species classification based on the UAV hyperspectral images

doi:10.13474/j.cnki.11-2246.2022.0320

Abstract

Abstract: In recent years, mangrove forest community species losses and functional degradation have become more and more serious. In order to timely and accurately extract the spatial pattern and distribution information of mangrove forest, this paper first extracts the vegetation area based on the UAV hyperspectral image of Futian mangrove nature reserve in Shenzhen using the normalized difference vegetation index and intertidal mangrove index, and then selected the band combination using the best index method. The pixel-based support vector machine classification (SVM) and object-oriented image classification (OOC) methods are used to accurately identify mangrove species. The experimental results show that the overall accuracy of SVM classification and OOC methods are 81.03%, and 85.58% respectively. In conclusion, The OOC methods can effectively remove the salt and pepper noise, makes full use of the spectral, shape and texture information of the object, and provides more accurate mangrove distribution information.

Key words: hyperspectral, unmanned aerial vehicle, mangrove forest, support vector machine, object-oriented classification

CLC Number:

P237

YI Lina, ZHANG Guifeng, WEI Zheng, WANG Mianqing, LIU Jinke, WANG Liujing. Mangrove forest species classification based on the UAV hyperspectral images[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 26-31.

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