A semi-supervised coral reef substrate classification method based on soft and hard collaborative decision making
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摘要: 珊瑚礁底质分类对海洋资源开发和海洋生态环境保护起到至关重要的作用。目前，深度学习语义分割方法在遥感图像分类领域应用广泛，但在底质分类方面的研究较少。由于基于全监督深度学习的方法中逐像素标注标签的成本较高，不适用于大规模、高频次的底质分类工作，基于半监督的深度学习方法能够有效利用已标注标签为无标签数据产生伪标签，从而有效降低人工成本，然而现有半监督方法的性能易受伪标签噪声的干扰。针对以上问题，本文提出了一种基于软硬协作决策的半监督底质分类方法。首先，利用多模型联合决策生成高质量的伪标签；然后，提出了一种能够顾及伪标签像素置信度的损失函数来指导模型进行训练；最后，采用软硬协作的决策方式得到精确的底质分类结果。在美属维尔京群岛圣克罗伊岛北部的巴克岛礁和夏威夷群岛的中途岛东南约400 km处的珍珠与爱马仕环礁的浅层底栖生物栖息地地图数据集上评估了本文方法的精度，实验结果表明，本文提出的方法与全监督学习方法精度相当，比主流的语义分割方法精度平均高3.08%，能够有效服务于珊瑚礁底质调查工作。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.
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图 1 数据集影像（a, b）与标签数据（c, d）（1 mi≈1.609 km）
Fig. 1 Data set image (a, b) with label data (c, d) (1 mi≈1.609 km)
图 2 基于多模型联合决策的伪标签生成
Fig. 2 Pseudo-label generation based on joint multi-model decision making
图 3 3CLoss指导下的软硬协作底质分类
Fig. 3 3CLoss-guided classification of soft and hard collaborative substrates
图 4 BIRNM、PHA数据集的参考结果（a, c）和本文方法底质分类结果（b, d）（1 mi≈1.609 km）
Fig. 4 Reference results of BIRNM, PHA datasets (a, c) and substrate classification results of the method in this paper (b, d) (1 mi≈1.609 km)
图 5 不同网络在BIRNM数据上的语义分割结果
Fig. 5 Semantic segmentation results of different networks on BIRNM data
图 6 不同方法在PHA数据集上的语义分割结果
Fig. 6 Semantic segmentation results of different networks on PHA dataset
图 7 不同底质分类方法整体效果图（左列为BIRNM，右列为PAH）（1 mi≈1.609 km）
Fig. 7 Overall effect of different substrate classification methods (the left column are BIRNM, right column are PAH) (1 mi≈1.609 km)
表 1 BIRNM数据集底质分类类型描述
Tab. 1 Description of substrate classification types in the BIRNM dataset
类别 描述 卫星 水下 珊瑚 连续的、高浮雕式的珊瑚形成，形状各异，包括平行于大陆架边缘的线性珊瑚 
碎屑 死去的、不稳定的珊瑚瓦砾，经常被丝状物或其他大型藻类所占据。这种底质经常出现在礁顶，珊瑚礁碎石可以在宽阔的近海沙地上以低密度聚集的方式出现 
岩石 从岛屿基岩延伸到海上的固体碳酸盐块的聚集，或从原生床剥离和运输的松散碳酸盐碎片，根据温特沃斯标准，单个巨石的直径在0.25～3 m之间 
砂 粗糙的沉积物，通常存在于海流或波浪能量影响的区域。颗粒大小在
1/16～256 mm不等
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表 2 巴克岛礁数据集半监督实验结果
Tab. 2 Results of semi-supervised experiments on the Buck Island dataset
方法 背景 珊瑚 碎屑 岩石 砂 mIoU Lawin 0.930 0 0.853 2 0.729 8 0.367 0 0.740 0 0.723 3 SegFormer 0.942 8 0.863 1 0.689 5 0.513 1 0.762 5 0.754 2 PanopticDeep 0.930 4 0.851 2 0.679 0 0.442 5 0.713 8 0.718 3 本文方法 0.939 5 0.869 1 0.726 1 0.539 9 0.784 7 0.771 9
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表 3 珍珠与爱马仕环礁半监督实验结果
Tab. 3 Results of semi-supervised experiments on the Pearl and Hermes Atoll
方法 背景 珊瑚 碎屑 砂 mIoU Lawin 0.874 1 0.675 5 0.528 5 0.452 1 0.632 5 SegFormer 0.872 5 0.661 2 0.523 1 0.437 1 0.623 5 PanopticDeep 0.866 6 0.672 6 0.497 5 0.443 1 0.619 9 本文方法 0.875 3 0.684 2 0.557 7 0.472 1 0.647 3
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表 4 3CLoss对实验精度的影响
Tab. 4 3CLoss effect on experimental accuracy
方法 Loss 背景 珊瑚 碎屑 岩石 砂 mIoU 本文方法 3CLoss 0.939 5 0.869 1 0.726 1 0.539 9 0.784 7 0.771 9 本文方法 Cross Entropy Loss 0.942 7 0.859 8 0.713 7 0.477 9 0.746 4 0.748 1
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表 5 迭代次数对平均交并比的影响
Tab. 5 Effect of number of iterations on mIoU
迭代次数 背景 珊瑚 碎屑 岩石 砂 mIoU 1 0.935 3 0.867 0 0.729 8 0.545 0 0.774 2 0.770 3 2 0.939 5 0.869 1 0.726 1 0.539 9 0.784 7 0.771 9 3 0.937 8 0.866 9 0.733 8 0.531 8 0.772 1 0.768 4
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表 6 本文方法与全监督语义分割方法对比结果
Tab. 6 Comparison results between the method in this paper and the fully supervised semantic segmentation method
方法 监督方法 mIoU（BIRNM） mIoU（PHA） Lawin 全监督 0.759 6 0.633 5 SegFormer 全监督 0.770 6 0.638 1 PanopticDeep 全监督 0.743 8 0.624 7 本文方法 半监督 0.771 9 0.647 3
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