Semantic segmentation of high spatial resolution images with deep neural networks

Abstract

Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.

Keywords:

• global context information
• high-resolution image segmentation
• deep learning
• residual network
• pyramid pooling

Acknowledgements

The authors would thank the ISPRS provided the Vaihingen and Potsdam datasets.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.html.

2. https://github.com/tornadomeet/ResNet.

3. http://www2.isprs.org/commissions/comm2/wg4/vaihingen-2d-semantic-labeling-contest.html.

4. http://www2.isprs.org/commissions/comm2/wg4/potsdam-2d-semantic-labeling.html.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China under Grant number 2017YFB0503600; the National Natural Science Foundation of China under Grant numbers 41601451, 41631179; Zhejiang Provincial Natural Science Foundation of China under Grant number LQ19D010006; the Natural Science Foundation of Beijing, China under Grant number 4172064; the Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province under Grant number OBDMA201512; and the International Partnership Program of the Chinese Academy of Sciences under Grant numbers 131C11KYSB20160061, 131551KYSB20160002.