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ABSTRACT
Semantic segmentation is a commonly used intelligent interpretation method. However, semantic segmentation via deep learning usually faces several problems: resolution loss due to downsampling, difficulty in obtaining context information, and imprecise object boundaries. These problems lead to a poor performance when using satellite images instead of conventional images and limit the value of intelligent interpretation of satellite images in practical application. In this study, we construct a Qilian Ecological Resource Extraction Dataset (QERED) with multiple scene satellite images of Qilian after preprocessing via correction, fusion, mosaic, and annotation. We then propose a segmentation network, namely, High-resolution Resource Extracting Network (HrreNet), by using high-resolution feature representation, multi-scale context fusion, boundary refinement with relearning, and structural similarity loss. Experiments on QERED show that HrreNet greatly improves performance on small-size objects and slightly improves performance on larger-size objects. HrreNet achieves the best result of 73.36% mIoU on a moderate-resolution satellite dataset CCFD and 80.87% mIoU on a high-resolution satellite dataset Vaihingen.
Acknowledgements
The authors thank the Natural Resources Remote Sensing Center of Qinghai Province for the original satellite images. We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.
Disclosure statement
No potential conflict of interest was reported by the author(s).