Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

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.

Automated summary

This paper explores the application of Convolutional Neural Networks (CNN) for semantic segmentation of remote sensing images and proposes two models, SegNet and U-net, with index pooling. By integrating these models, an algorithm is presented which can achieve better multi-target segmentation compared to using the two models individually.