基于深度卷积网络的高分遥感图像语义分割

随着我国高分对地观测系统的不断发展，对高分影像智能化分析与处理的应用需求愈来愈多，基于深度学习语义分割的影像分类也受到高度关注。作为近景图像语义分割的热点模型，Deeplab网络在应用时取得了良好的效果。为了解决多尺度高分辨率遥感影像语义分割问题，本文首先利用空洞卷积扩大Atrous空间金字塔池化（ASPP）结构的感受野，然后对DeepLabv3进行改进并将其用于高分2号遥感影像的分类处理。我们以郴州地区的高分遥感影像为研究对方法进行了验证，首先对原始影像进行预处理，再对预处理图像进行数据增强与扩充，最后通过对不同参数条件下的分类结果进行对比，分析该模型的适应性和精确性。在我们的数据集中，本文方法的实验分类像素精度为88.2％，MIoU达到72.5％，得到了比Deeplab更好的分类效果。 

Semantic Segmentation of High-Resolution Remote Sensing Image Based on Deep Convolutional Network

With the continuous development of high- resolution earth observation system in China, more and more applications for high-resolution image intelligent analysis and processing are required. The semantic segmentation based on deep learning has attracted people's attention too. As a research hotspot of close-range image semantic segmentation, the Deeplab network has achieved good results. In order to solve the problem of multi-scale and high-resolution remote sensing image semantic segmentation, this paper firstly expands the receptive field of Atrous spatial pyramid pooling (ASPP) by using dilated convolution, then improves deeplabv3 model and applies it to the classification processing of high-resolution NO.2 (GF-2) remote sensing images. We take the GF-2 remote sensing image of Chenzhou area as the research objects to verify the method. First, we preprocess the original image, then enhance and expand the data of the preprocessed image. Finally, we compare the classification results under different parameters, and analyze the adaptability and accuracy of the model. In our data set, the experimental classification accuracy of this method is 88.2%, and the MIoU is 72.5%, which is better than that of Deeplab.