利用高分辨率光谱仪在实地测得的光谱数据来识别新疆阜康地区的 7 种典型荒漠草种,对原始高光谱数据作预处理(微分和平滑),选取典型荒漠植被的光谱特征(红边、绿峰、红谷、RVI 等)作为输入数据,植被类型作为输出数据,构建基于 BP 神经网络模型的典型荒漠草地分类器,进行了三组基于高光谱特征的草地类型分类实验,蛄果表明:(1)红边特征较其余吸收特征更能获得精确的分类结果;(2)波段 550～790 nm 间的窄波段光谱分类间隔中,20 nm 优于 10 nm 的间隔;(3)草地分类器中 BP 网络模型的输入层、隐藏层神经元个数与 BP 网络训练时间、精度具有复杂的耦合关系,不可一概而论.%In order to identify the seven typical desert grasses of Xinjiang Fukang area,high-resolution spectroscopy is used to obtain the hyper-spectral data. After the preprocessing of the original hyper-spectral data,such as differentiation and smoothing,the typical desert grass classifier based on BP neural network is constructed, with the input data of typical desert grasses'spectral characters(red-edge,green peak, red valley, RVI,etc.) and the output data of vegetation types.Three groups of grass classification experiments based on hyper-spectral features demonstrate that: (1)Red-edge characteristics perform better than the other absorption features to obtain accurate classification results.(2)Between the narrow-band spectral classification interval 550～790nm,interval 20nm performs better than interval 10nm.(3)There are complex relationships between the input,output layers of BP neural network and the training time,precision of BP network.
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