Abstract
Recently, many segmentation methods based on supervised deep learning have been widely used in remote sensing images. However, these approaches often require a large number of labeled samples, which is difficult to obtain them for remote sensing images. Self-supervision is a new learning paradigm, and can solve the problem of lack of labeled samples. In this method, a large number of unlabeled samples are employed for pre-training, and then a few of labeled samples are leveraged for downstream tasks. Contrast learning is a typical self-supervised learning method. Inspired, we propose a Dense Multi-scale Feature Contrastive Learning Network (DMF-CLNet), which is divided into global and local feature extraction parts. Firstly, in the global part, instead of traditional ASPP, DenseASPP can obtain more context information of remote sensing images in a dense way without increasing parameters. Secondly, in the global and local parts, Coordinate Attention (CA) modules are introduced respectively to improve the overall performance of the segmentation model. Thirdly, in the global and local parts, the perceptual loss is calculated to extract deeper features. Two remote sensing image segmentation datasets are evaluated. The experimental results show that our model is superior to the current self-supervised contrastive learning methods and ImageNet pre-training techniques.
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Acknowledgement
This research was supported by the National Natural Science Foundation of China (No. 61901537), Research Funds for Overseas Students in Henan Province, China Postdoctoral Science Foundation (No. 2020M672274), Science and Technology Guiding Project of China National Textile and Apparel Council (No. 2019059), Postdoctoral Research Sponsorship in Henan Province (No. 19030018), Program of Young backbone teachers in Zhongyuan University of Technology (No. 2019XQG04), Training Program of Young Master's Supervisor in Zhongyuan University of Technology (No. SD202207).
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Song, M., Li, B., Wei, P., Shao, Z., Wang, J., Huang, J. (2022). DMF-CL: Dense Multi-scale Feature Contrastive Learning for Semantic Segmentation of Remote-Sensing Images. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_13
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