A semi-supervised coral reef substrate classification method based on soft and hard collaborative decision making

Yu Jun; Chen Hui; Zhu Daming; Cheng Liang; Duan Zhixin; Zhuang Qizhi; Chu Sensen; Yang Wei; Du Siyu

doi:10.12284/hyxb2023049

Volume 45 Issue 4

Mar. 2023

Turn off MathJax

Article Contents

Article Navigation > Haiyang Xuebao > 2023 > 45(4): 154-164

Yu Jun，Chen Hui，Zhu Daming, et al. A semi-supervised coral reef substrate classification method based on soft and hard collaborative decision making[J]. Haiyang Xuebao，2023, 45(4)：154–164 doi: 10.12284/hyxb2023049

Citation:

PDF( 2247 KB)

A semi-supervised coral reef substrate classification method based on soft and hard collaborative decision making

doi: 10.12284/hyxb2023049

1.
Faculty of Land and Resources Engineering, Kunming University of Technology, Kunming 650093, China
2.
School of Geography and Marine Science, Nanjing University, Nanjing 210023, China

Received Date: 2022-08-16
Rev Recd Date: 2022-10-22

Available Online: 2023-03-29

Publish Date: 2023-03-31

Abstract

Abstract

Coral reef substrate classification plays a crucial role in marine resource development and marine ecological protection. At present, deep learning semantic segmentation methods are widely used in the field of remote sensing image classification, but less research has been conducted in substrate classification. Due to the high cost of pixel-by-pixel labeling in the fully supervised deep learning-based method, it is not suitable for large-scale and high-frequency substrate classification work. The semi-supervised deep learning-based method can effectively use the labeled labels to generate pseudo-labels for unlabeled data, thus effectively reducing the labor cost, however, the performance of the existing semi-supervised method is vulnerable to the interference of pseudo-label noise. To address the above problems, this paper proposes a semi-supervised substrate classification method based on soft and hard collaborative decision making. First, a high quality Pseudo tag is generated using joint decision making of multiple models; then, a loss function (Collaboration Choice of decision Confidence Loss function, 3CLoss) is proposed to take into account the confidence of Pseudo tag pixels and guide the model for training; finally, a soft and hard collaborative decision making approach is used to obtain accurate substrate classification results. The accuracy of this paper was evaluated on the shallow benthic habitat atlas datasets of Buck Island Reef in the northern part of St. Croix, U.S. Virgin Islands, and Pearl and Hermes Atolls, about 400 km southeast of Midway Island, Hawaiian Islands, and the experimental results show that the accuracy of the proposed method is comparable to that of the fully supervised learning method, and 3.08% higher than that of the mainstream semantic segmentation methods on average, which can effectively serve the coral reef substrate survey.
- seabed substrate classification,
- soft and hard collaboration,
- semantic segmentation,
- semi-supervised learning,
- fully supervised learning,
- remote sensing,
- coral reefs,
- convolutional neural networks

FullText(HTML)

References(24)

References

[1]	逄岩, 许枫, 刘佳. 基于Gammatone滤波器组时频谱和卷积神经网络的海底底质分类[J]. 应用声学, 2021, 40(4): 510−517. doi: 10.11684/j.issn.1000-310X.2021.04.003 Pang Yan, Xu Feng, Liu Jia. Seabed sediment classification based on Gammatone filter banks time-frequency spectrum and convolutional neural networks[J]. Journal of Applied Acoustics, 2021, 40(4): 510−517. doi: 10.11684/j.issn.1000-310X.2021.04.003
[2]	Gregr E J, Haggarty D R, Davies S C, et al. Comprehensive marine substrate classification applied to Canada’s Pacific shelf[J]. PLoS One, 2021, 16(10): e0259156. doi: 10.1371/journal.pone.0259156
[3]	Reshitnyk L, Costa M, Robinson C, et al. Evaluation of WorldView-2 and acoustic remote sensing for mapping benthic habitats in temperate coastal Pacific waters[J]. Remote Sensing of Environment, 2014, 153: 7−23. doi: 10.1016/j.rse.2014.07.016
[4]	Wicaksono P, Aryaguna P A. Analyses of inter-class spectral separability and classification accuracy of benthic habitat mapping using multispectral image[J]. Remote Sensing Applications: Society and Environment, 2020, 19: 100335. doi: 10.1016/j.rsase.2020.100335
[5]	Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1977, 39(1): 1−22. doi: 10.1111/j.2517-6161.1977.tb01600.x
[6]	Pillay T, Cawthra H C, Lombard A T. Characterisation of seafloor substrate using advanced processing of multibeam bathymetry, backscatter, and sidescan sonar in Table Bay, South Africa[J]. Marine Geology, 2020, 429: 106332. doi: 10.1016/j.margeo.2020.106332
[7]	Blaschke T. Object based image analysis for remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(1): 2−16. doi: 10.1016/j.isprsjprs.2009.06.004
[8]	Furey T S, Cristianini N, Duffy N, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data[J]. Bioinformatics, 2000, 16(10): 906−914. doi: 10.1093/bioinformatics/16.10.906
[9]	Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5−32. doi: 10.1023/A:1010933404324
[10]	万佳馨, 任广波, 马毅. 基于WorldView-2和GF-2遥感影像的赵述岛礁坪底质变化研究[J]. 海洋科学, 2019, 43(10): 43−54. Wan Jiaxin, Ren Guangbo, Ma Yi. Study on substrate changes of Zhaoshu reef flat based on WorldView-2 and GF-2 remote sensing images[J]. Marine Sciences, 2019, 43(10): 43−54.
[11]	逄今朝, 任广波, 施祺, 等. 基于底质类型变化监测的2005−2018年西沙永乐群岛珊瑚礁白化分析[J]. 海洋科学, 2021, 45(6): 92−106. Pang Jinzhao, Ren Guangbo, Shi Qi, et al. Analysis of coral reef bleaching in Yongle Islands of Xisha from 2005 to 2018 based on sediment types change monitoring[J]. Marine Sciences, 2021, 45(6): 92−106.
[12]	董娟, 任广波, 胡亚斌, 等. 基于高分辨率遥感的珊瑚礁地貌单元体系构建和分类方法——以8波段Worldview-2影像为例[J]. 热带海洋学报, 2020, 39(4): 116−129. Dong Juan, Ren Guangbo, Hu Yabin, et al. Construction and classification of coral reef geomorphic unit system based on high-resolution remote sensing: using 8-band Worldview-2 Image as an example[J]. Journal of Tropical Oceanography, 2020, 39(4): 116−129.
[13]	李晓敏, 马毅, 吕喜玺. 南海珊瑚岛礁遥感分类体系和解译标志[J]. 海洋科学, 2021, 45(5): 23−30. doi: 10.11759/hykx20201110003 Li Xiaomin, Ma Yi, Lü Xixi. Establishing a remote sensing classification system and interpretation marks for the coral islands and reefs in the South China Sea[J]. Marine Sciences, 2021, 45(5): 23−30. doi: 10.11759/hykx20201110003
[14]	Wan Jiaxin, Ma Yi. Multi-scale spectral-spatial remote sensing classification of coral reef habitats using CNN-SVM[J]. Journal of Coastal Research, 2020, 102(S1): 11−20.
[15]	Huang Rongyong, Zhang Huiya, Yu Kefu. Analysis on the live coral cover around Weizhou Island using MODIS data[J]. Sensors, 2019, 19(19): 4309. doi: 10.3390/s19194309
[16]	谭琨, 王雪, 杜培军. 结合深度学习和半监督学习的遥感影像分类进展[J]. 中国图象图形学报, 2019, 24(11): 1823−1841. doi: 10.11834/jig.190348 Tan Kun, Wang Xue, Du Peijun. Research progress of the remote sensing classification combining deep learning and semi-supervised learning[J]. Journal of Image and Graphics, 2019, 24(11): 1823−1841. doi: 10.11834/jig.190348
[17]	King A, Bhandarkar S M, Hopkinson B M. A comparison of deep learning methods for semantic segmentation of coral reef survey images[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 1394−1402.
[18]	Li Jiwei, Knapp D E, Fabina N S, et al. A global coral reef probability map generated using convolutional neural networks[J]. Coral Reefs, 2020, 39(6): 1805−1815. doi: 10.1007/s00338-020-02005-6
[19]	Wang Mengqiu, Hu Chuanmin. Satellite remote sensing of pelagic Sargassum macroalgae: the power of high resolution and deep learning[J]. Remote Sensing of Environment, 2021, 264: 112631. doi: 10.1016/j.rse.2021.112631
[20]	耿艳磊, 陶超, 沈靖, 等. 高分辨率遥感影像语义分割的半监督全卷积网络法[J]. 测绘学报, 2020, 49(4): 499−508. Geng Yanlei, Tao Chao, Shen Jing, et al. High-resolution remote sensing image semantic segmentation based on semi-supervised full convolution network method[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(4): 499−508.
[21]	李鑫伟, 李彦胜, 张永军. 弱监督深度语义分割网络的多源遥感影像水体检测[J]. 中国图象图形学报, 2021, 26(12): 3015−3026. Li Xinwei, Li Yansheng, Zhang Yongjun. Weakly supervised deep semantic segmentation network for water body extraction based on multi-source remote sensing imagery[J]. Journal of Image and Graphics, 2021, 26(12): 3015−3026.
[22]	Xie Enze, Wang Wenhai, Yu Zhiding, et al. SegFormer: simple and efficient design for semantic segmentation with transformers[J]. Advances in Neural Information Processing Systems, 2021, 34: 12077−12090.
[23]	Cheng Bowen, Collins M D, Zhu Yukun, et al. Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. New York: IEEE, 2020: 12475−12485.
[24]	Yan Haotian, Zhang Chuang, Wu Ming. Lawin transformer: improving semantic segmentation transformer with multi-scale representations via large window attention[EB/OL]. (2022–01–05)[2022–08–15]. https://arxiv.org/abs/2201.01615.