为了探究深度学习方法基于多时相高分1号影像的森林植被分类效果。以黑龙江省孟家岗林场为研究区,以多时相GF-1影像和数字高程模型(DEM)为数据源,通过提取光谱特征、植被指数、纹理特征以及地形特征构建多特征数据集,并结合VSURF算法进行特征优选。同时,分别采用优化后的U-Net,SegNet,DeepLab V3+模型对森林林分类型进行分类,并与最大似然法、随机森林方法进行对比分析。结果表明:1)利用多时相影像分类精度明显优于单时相影像;2)基于VSURF算法从构建的97个特征中优选出16个特征变量,其中NDVI、RVI、均值、同质性、对比度、相关性以及DEM特征具有较高的贡献性均被保留,其余变量被消除,从而在一定程度上避免“维数灾难”,提高模型效率;3)3种深度学习方法中U-Net模型的分类精度最高,总体精度为86.04%,Kappa系数为0.742,DeepLab V3+模型次之,SegNet模型精度最低。同时,深度学习方法的精度均优于随机森林和最大似然法。基于多时相GF-1影像构建最优特征组合,并结合深度学习方法对森林林分类型分类具有一定的参考价值。

In order to explore the effect of deep learning method on forest vegetation classification based on multi-temporal GF-1 images. This paper took Mengjiagang Forest Farm in Heilongjiang Province as the research area,took multi-temporal GF-1 images and Digital Elevation Model (DEM) as data sources,and constructed a multi-feature data set by extracting spectral features,vegetation index,texture features and topographic features,and combined with VSURF algorithm for feature optimization. At the same time,the optimized U-Net,SegNet,and DeepLab V3+models were used to classify the forest stand types,and compared with the maximum likelihood method and random forest method. The results showed as follows: 1) The classification accuracy of multi-temporal images was significantly better than that of single-temporal images; 2)Based on VSURF algorithm,16 feature variables were selected from the 97 features constructed,in which NDVI,RVI,mean,homogeneity,contrast,correlation and DEM features were retained because of their high contribution,and the other variables were eliminated,so as to avoid the "dimension disaster" to a certain extent and improve the efficiency of the model;3) Among the three depth learning methods,U-Net model had the highest classification accuracy,with an overall accuracy of 87.18%,kappa coefficient was 0.710,DeepLab V3+model followed,and SegNet model had the lowest accuracy. Constructing the optimal feature combination based on multi-temporal GF-1 images,combined with deep learning methods,has certain reference value for the classification of forest stand types.