基于投影寻踪学习网络算法的植物群落高分遥感分类研究

Research on Classification of Plant Community Using Projection Pursuit Learning Network Algorithm on High Resolution Remote Sensing Images

传统的植物群落调查方法主要是野外样地调查和抽样统计,其对于地形复杂的区域难以做到对数据的全面调查;将遥感技术应用于植物群落调查,可实现数据的全面获取,以及对植物群落的快速分类。在深圳市植物群落野外样地调查的基础上,本文应用高分辨率Pléiades影像,结合光谱、地形及纹理信息,采用投影寻踪学习网络的方法,实现了深圳市东部地区植物分类。在实验中,选取人工林和次生林中典型群落样本,将投影寻踪与学习网络算法结合应用于植被分类,通过分类结果与经典监督分类方法比较表明,该算法应用于植物群落分类是可行的;并且该算法分类精度高,更新速度快,能满足深圳市重点项目基本生态控制线专项调查的要求。

Plant community is a significant content in the ecosystem. Traditional investigation method for plant community is mainly based on statistical sampling, which is limited by the data acquisition from complex terrain areas. In contrast, highresolution remote sensing technique provides a convenient way to quickly access data in a large area. To overcome the shortcomings derived from the high dimensional features, which is caused by related data increasing, we choose the algorithm of projection pursuit learning network（PPLN） along with field samples of typical plant communities to realize a fast classification on the vegetation in the east of Shenzhen. Then,in the experiment, the spectral and texture information extracted from Pléiades images,and the terrain interpolated from topographic map are selected and used to build high dimensional features, which is crucial to the vegetation classification using remote sensing images. The learning network for projection pursuit is applied to discriminating the typical communities in both plantation and natural secondary forest in the study area. Compared with Maximum- likelihood classification（MLC） and Support Vector Machine（SVM）, PPLN can achieve more accurate results for plant community classification. As a conclusion, the plant community classification with PPLN meets the requirements of the investigation project,achieves the quick updating of some basic information related to forest resources, and looks forward to involve in some other ecological research as well.