A style transfer method for shipwrecks and crashed aircraft based on whitening and coloring transformation

doi:10.13474/j.cnki.11-2246.2025.0613

Abstract

Abstract: Deep learning algorithms have been widely applied in the field of image classification. However, the limited availability of side-scan sonar images containing target objects poses a significant challenge in meeting the training demands of these algorithms, often leading to issues like overfitting. Style transfer has emerged as an effective method for augmenting training samples. This paper investigates and reconstructs the iterative processes of the WCT and PhotoWCT style transfer algorithms. Based on the characteristics of Unpooling and Upsampling, we propose modifications to the WCT algorithm across different feature layers of the decoder, resulting in the development of the WCST style transfer algorithm, which is more suited for side-scan sonar imagery.Using the WCST algorithm, realistic pseudo side-scan sonar images containing target objects were generated to meet the training requirements of image classification networks. In subsequent image classification experiments, datasets generated by WCST and PhotoWCT were used to train ResNet50. The results demonstrated that WCST outperformed the other methods in all accuracy metrics, highlighting its effectiveness in augmenting high-quality training sets for side-scan sonar image classification.

Key words: style transfer, side-scan sonar images, deep learning, image classification

CLC Number:

P237

YAN Baiyu, ZHAI Guojun, BIAN Shaofeng. A style transfer method for shipwrecks and crashed aircraft based on whitening and coloring transformation[J]. Bulletin of Surveying and Mapping, 2025, 0(6): 73-77.

References

[1] HUO Guanying,WU Ziyin,LI Jiabiao.Underwater object classification in sidescan sonar images using deep transfer learning and semisynthetic training data[J].IEEE Access,2020,8: 47407-47418.
[2] YE Xiufen,YANG Haibo,LI Chuanlong,et al.A gray scale correction method for side-scan sonar images based on retinex[J].Remote Sensing,2019,11(11): 1281.
[3] 吕思晨,梁红,杨长生.基于迁移学习的声呐图像分类方法研究[C]//2022年中国西部声学学术交流会论文集.哈尔滨:[s.n.],2022.
[4] 陈佳辉,陈岚萍,夏小云,等.基于迁移学习的海底底质声呐图像分类[J].计算机仿真,2022,39(1):229-233.
[5] FU Chengyang,LIU Wei,RANGA A,et al.DSSD: deconvolutional single shot detector[EB/OL].[2024-09-20].https://arxiv.org/abs/1701.06659v1.
[6] 朱世伟,杭仁龙,刘青山.基于类加权YOLO网络的水下目标检测[J].南京师大学报(自然科学版),2020,43(1): 129-135.
[7] 于淼.基于深度学习的侧扫声呐图像目标检测方法研究[D].哈尔滨: 哈尔滨工程大学,2020.
[8] NEUPANE D,SEOK J.A review on deep learning-based approaches for automatic sonar target recognition[J].Electronics,2020,9(11): 1972.
[9] LI Yijun,FANG Chen,YANG Jimei,et al.Universal style transfer via feature transforms[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.[S.l.]:ACM Press,2017:385-395.
[10] WINNEMÖLLER H,OLSEN S C,GOOCH B.Real-time video abstraction[J].ACM Transactions on Graphics,2006,25(3): 1221-1226.
[11] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//Proceedings of 2016 International Conference on Learning Representations.[S.l.]:Computer Science,2016.
[12] LI Y,LIU M-Y,LI X,et al.A closed-form solution to photorealistic image stylization[C]//Proceedings of 2018 European Conference on Computer Vision (ECCV).[S.l.]:ECCV,2018.
[13] NOH H,HONG S,HAN B.Learning deconvolution network for semantic segmentation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV).Santiago:IEEE,2015: 1520-1528.
[14] LI Chuanlong,YE Xiufen,XI Jier,et al.A texture feature removal network for sonar image classification and detection[J].Remote Sensing,2023,15(3): 616.
[15] XIA Guisong,BAI Xiang,DING Jian,et al.DOTA: a large-scale dataset for object detection in aerial images[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City: IEEE,2018: 3974-3983.
[16] ZHU Haigang,CHEN Xiaogang,DAI Weiqun,et al.Orientation robust object detection in aerial images using deep convolutional neural network[C]//Proceedings of 2015 IEEE International Conference on Image Processing (ICIP).Quebec City: IEEE,2015:3735-3739.
[17] CHENG Gong,HAN Junwei.A survey on object detection in optical remote sensing images[J].ISPRS Journal of Photogrammetry and Remote Sensing,2016,117: 11-28.
[18] LONG Yang,GONG Yiping,XIAO Zhifeng,et al.Accurate object localization in remote sensing images based on convolutional neural networks[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(5): 2486-2498.
[19] CHENG Gong,HAN Junwei,LU Xiaoqiang.Remote sensing image scene classification: benchmark and state of the art[J].Proceedings of the IEEE,2017,105(10): 1865-1883.
[20] ZHANG P,TANG J,ZHONG H,et al.Self-trained target detection of radar and sonar images using automatic deep learning[J].IEEE Transactions on Geoscience and Remote Sensing,2021,60:4701914.
[21] HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al.Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las Vegas:IEEE,2016: 770-778.
[22] FELDENS P.Super resolution by deep learning improves boulder detection in side scan sonar backscatter mosaics[J].Remote Sensing,2020,12(14): 2284.