Imbalance satellite image colorization with semantic salience priors

Chengyu Zheng; Yu Fu; Zian Zhao; Chenglong Wang; Jie Nie

doi:10.1117/12.2589397

27 January 2021 Imbalance satellite image colorization with semantic salience priors

Chengyu Zheng, Yu Fu, Zian Zhao, Chenglong Wang, Jie Nie

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117201U (2021) https://doi.org/10.1117/12.2589397
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Considering the low spatial resolution of remote sensing images, it is unreliable to achieve accurate pixel-level color restoration. Inspirited by human perception, which is sensitive to salience features, in this paper, we propose an approach for colorization of remote sensing images with semantic salience priors. Firstly, based on the DCGAN architecture, we introduce the semantic salience prior, which is designed and learned from existing data set with semantic labels to supervise the training of the network. Then, to eliminate the distortion in foreground color caused by the overwhelming amount of marine or bare land backgrounds, we leverage the idea of focal loss to prevent the vast number of backgrounds from overwhelming the generator. Finally, we evaluate the proposed method on the NWPU-RESISC45 public data set. Both the evaluations and comparisons validate the proposed colorization approach is superior to the state-of-the-art methods on remote sensing images.

Citation Download Citation

Chengyu Zheng, Yu Fu, Zian Zhao, Chenglong Wang, and Jie Nie "Imbalance satellite image colorization with semantic salience priors", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117201U (27 January 2021); https://doi.org/10.1117/12.2589397

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