In recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

近年来，深度学习在自然场景图像处理中的成功推动了其在遥感图像分析中的应用。在本文中，我们将卷积神经网络（CNN）应用于遥感图像的语义分割。我们通过索引池和U-net改进了Encoder-Decoder CNN结构SegNet，使其适合于遥感图像的多目标语义分割。结果表明，这两种模型在不同对象的分割上各有优缺点。另外，我们提出了一种整合这两个模型的整合算法。实验结果表明，与两种模型相比，本文提出的集成算法可以充分利用两种模型的多目标分割优势，实现更好的分割效果。