Projection pursuit learning network algorithm for plant classification

Plant community is a significant content in the ecosystem. Traditional investigation method for plant community is mainly based on field sampling, which is limited by the data acquisition from complex terrain areas. In contrast, high-resolution remote sensing technique provides a convenient way to quickly access data in a large area. higher dimensional information is needed to distinguish more fine features. 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.