基于FCN和面向对象的高分辨率遥感影像土地覆盖分类

Land cover classification of high resolution remote sensing imagery based on FCN and object-oriented technology

针对基于全卷积神经网络(FCN)进行遥感影像语义分割时,FCN的上、下采样机制会导致分割结果中地物边缘细节信息丢失的问题,提出一种基于面向对象分割结果优化FCN分类的高分遥感影像土地覆盖分类方法。基于FCN网络对高分遥感影像进行初始分类,并利用面向对象的分割结果优化基于FCN的初始分类结果。该方法不仅可以有效保留地物边缘细节信息,还可以有效消除FCN初始提取结果中存在的椒盐现象,优化分类结果的视觉效果,并提高分类精度。

In semantic segmentation of remote sensing imagery based on full convolutional neural network(FCN),the upsampling and downsampling mechanism in FCN led to the loss of edge details of ground objects in segmentation results.To solve this problem,a land cover classification method based on object-oriented segmentation results to optimize FCN classification of high-resolution remote sensing images was proposed.Firstly,the high-resolution remote sensing imagery was initially classified by FCN model.Then,objectoriented segmentation results were used to optimize the initial classification results obtained based on FCN.Experimental results showed that the method in our paper could not only retain the details of the ground object edge effectively,but also eliminate the phenomenon of salt and pepper in the initial extraction results of FCN.Finally,the visual effect of the classification results was optimized and the classification accuracy was improved.