Lite-HRNet-OCR: a lightweight high-resolution network and object-contextual representation for road extraction on remote sensing images

Xuemei Chen; Zhiheng Liu; Suiping Zhou; Hang Yu; Jixuan Chen; Yanming Liu

doi:10.1117/12.2668135

23 February 2023 Lite-HRNet-OCR: a lightweight high-resolution network and object-contextual representation for road extraction on remote sensing images

Xuemei Chen, Zhiheng Liu, Suiping Zhou, Hang Yu, Jixuan Chen, Yanming Liu

Author Affiliations +

Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 125511D (2023) https://doi.org/10.1117/12.2668135
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China

Abstract

The extraction of roads from satellite images is a necessary step for urban planning, intelligent transportation, etc. We propose Lite-HRNet-OCR, a lightweight and efficient CNN structure for road segmentation. The network of Lite-HRNet-OCR begins with a lightweight Lite-HRNet backbone that learns the weights of all channels and resolutions. The weights serve as the channel for information exchange across channels and resolutions. The multi-resolution output of the lightweight backbone is input to OCRNet, which organizes contextual pixels into object regions and exploits the relationships between pixels and object regions to augment the representation of their pixels. Two loss functions, cross-entropy loss and Tversky loss, are used to solve the problem of sample imbalance. Experimental results show that our method achieves competitive performance on the public CHN6-CUG road dataset. Specifically, the Lite-HRNet-OCR achieves 64.39% Mean IOU and 96.52% F1 with 2.9 MParams and 29.4 GFLOPs.

Citation Download Citation

Xuemei Chen, Zhiheng Liu, Suiping Zhou, Hang Yu, Jixuan Chen, and Yanming Liu "Lite-HRNet-OCR: a lightweight high-resolution network and object-contextual representation for road extraction on remote sensing images", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 125511D (23 February 2023); https://doi.org/10.1117/12.2668135

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KEYWORDS

Roads

Image segmentation

Semantics

Optical character recognition

Remote sensing

Convolution

Data modeling

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