黄河口湿地典型地物类型高光谱分类方法

黄河口湿地地物类型具有复杂多样的特点,本文将线性光谱混合分析模型与归一化植被指数(NDVI)和归一化水体指数(NDWI)相结合,建立了一种新的滨海湿地遥感影像分类方法;开展了基于CHRIS高光谱影像的黄河口湿地芦苇、柽柳、碱蓬、大米草、潮滩和水体6种典型地物分类实验,整体分类精度为77.33%,Kappa 系数为 0.71,与经典的最大似然分类(MLC)方法相比较,整体分类精度提高1.6%,Kappa 系数提高0.02,尤其是芦苇、碱蓬、大米草和潮滩的分类精度明显提高。

Classification method of hyperspectral image in typical surface feature of Huanghe River estuary wetland

The typical surface feature of Huanghe River estuary wetland is complex and diverse. In this study, a new classification model for coast wetland remote image was constructed using the linear spectral mixture analysis model, combined with normalized difference vegetation index(NDVI) and normalized difference water index(NDWI). Based on CHRIS hyperspectral image, a classification test of Huanghe River estuary wetland was carried, which consisted of 6 kinds of typical objects: phragmites, tamarix chinesis, suaeda, spartina, tidal flat and water, The results show that the overall accuracy of the combined model is 77.33%, and Kappa coefficient is 0.71, increasing 1.6% and 0.02 respectively compared with that from the classical MLC method, and especially, a better classification accuracy is obtained obviously for phragmites, suaeda, spartina and tidal flat.