基于HyMap数据的浮水植被信息提取

Extraction of floating-leaved vegetation information based on HyMap data

以美国加利福尼亚州萨克拉门托—圣华金三角洲为研究区,利用2007年6月空间分辨率3 m的HyMap高光谱数据,根据湿地植被的光谱差异,结合地面实况数据,对植被的"三边"参数进行分析,选取合适的植被指数并结合"三边"参数特征,构建决策树模型,提取出研究区的浮水植被,并与最大似然法的分类结果进行比较。结果表明:利用决策树模型分类的总体精度达到82.68%,与最大似然法相比,总精度提高了6%,很好地识别出了研究区湿地植被中的浮水植被。

In this paper, Sacramento, California - San Joaquin River Delta was taken as the study area, and HyMap hyperspectral data with 3 m spatial resolution acquired in June 2007 combined with ground truth data were used for pattern recognition of floating-leaved vegetation in the study area. The study was based on the spectral differences of wetland vegetations, and the “trilateral” parameters of vegetation were analyzed. Then the authors selected suitable vegetation indices combined with “trilateral” parameter features and built a decision tree model to extract the floating-leaved vegetation of the study area in comparison with the maximum likelihood classification results. The results show that the use of decision tree classification model can achieve overall accuracy of 82.68%, and that, compared with the maximum likelihood method, the total accuracy was improved by 6%, which can well identify the floating-leaved vegetation in the wetland vegetation of the study area.