应用多维遥感信息协同的森林树种分类

Classification of Forest Tree Species Using Multi-dimensional Remote Sensing Information Coordination

为了探讨影响森林树种分类主要遥感信息,分析协同不同维度信息进行树种分类的差异。以美国俄勒冈州波特兰市东南部黑豹溪流域天然林区森林树种为研究对象,采用主成分分析和人工蚁群方法筛选机载高光谱影像特征波段、纹理特征与植被指数,同时利用机载雷达(LiDAR)数据提取森林垂直结构参数,并使用随机森林方法进行树种分类。结果表明:利用一维信息平均分类精度为69.86%,其中,利用纹理特征进行分类的精度最高(77.40%),垂直结构参数分类的精度最低(62.44%);二维信息组合平均分类精度为79.68%,高光谱特征波段与纹理特征组合的分类精度最高;三维信息组合平均分类精度达到85.00%,高光谱特征波段、纹理特征和植被指数协同的分类精度最高;全部四维信息协同分类时的精度高于一维、二维与三维信息协同时的分类精度,分类精度89.20%。由此可见,协同多维遥感信息能够有效提高森林树种分类精度,其中基于高光谱数据提取的纹理信息与特征光谱信息在森林树种分类中起到了重要作用,而协同垂直结构信息进一步提高了森林树种分类的精度。

In order to explore the main remote sensing information affecting forest tree species classification, the difference of tree species classification based on different dimension information were analyzed. The research object is the forest tree species in the natural forest area of Panther Creek in the southeast of Portland, Oregon, United States. The principal component analysis and ant colony optimization method were used to select the feature bands, texture features and vegetation indexes of airborne hyperspectral images. The LiDAR data were used to extract the vertical structure parameters of forests, and the random forest method was used to classify tree species. The results showed that the average classification accuracy using one-dimensional information was 69.86%, of which the classification accuracy using texture features was the highest(77.40%), and the classification accuracy using vertical structure parameters was the lowest(62.44%). The average classification accuracy of two-dimensional information combination was 79.68%, and the classification accuracy of hyperspectral feature band and texture feature combination was the highest. The average classification accuracy of three-dimensional information combination was 85.00%, and the classification accuracy of hyperspectral feature band, texture feature and vegetation index was the highest. The classification accuracy of all four-dimensional information was higher than that of one-dimensional, two-dimensional and three-dimensional information, and the classification accuracy was 89.20%. It can be seen that collaborative multi-dimensional remote sensing information can effectively improve the classification accuracy of forest tree species, among which texture information and feature spectrum information extracted from hyperspectral data play an important role in forest tree species classification, while the collaborative vertical structure information further improves the accuracy of forest tree species classification.