摘要：

一幅遥感图像通常包含多种地物, 具有多个语义信息。使用多标签学习方法对遥感图像进行分类, 能更好地理解图像语义。然而, 由于人工标注具有主观性以及遥感图像目标具有复杂性, 导致图像标注不准确, 从而引入噪声(加性噪声)或出现标签缺失现象(减性噪声), 这2类噪声统称为混合噪声, 它们的存在会误导算法训练过程, 降低分类性能。针对混合噪声问题, 提出一种遥感图像多标签分类算法。首先对图像分别进行强增强和弱增强变换, 并将2种增强图像分别送入2个具有相同结构的网络中进行协同学习; 接着, 通过在训练过程中约束2幅图像内的一致性和图像间以及对应标签的结构一致性, 并将2种约束与二元交叉熵(BCE)损失相结合, 以形成最终的损失函数; 最后, 基于预测标签定义排序误差, 识别和矫正损失函数中的噪声标签, 从而提高模型鲁棒性。为验证所提方法的性能, 在遥感图像多标签数据集AID、UCM和DFC15上进行混合噪声多标签分类实验, 并从图像分类指标和多标签分类指标2个方面将所提方法与多种多标签分类方法进行对比。结果表明, 在不同标签噪声比下所提方法的总体性能最优。

关键词: 遥感图像, 多标签分类, 混合噪声, 协同学习, 标签矫正

Abstract:

Remote sensing images usually contain multiple land features and semantic information. Using multi-label learning methods to classify remote sensing images can improve the understanding of image semantics. However, owing to the subjectivity of manual annotation and the complexity of remote sensing image targets, inaccurate image annotation can lead to the introduction of noise (additive noise) or label loss (subtractive noise)—collectively referred to as mixed noise. Their presence can mislead the algorithm training process and reduce classification performance. A multi-label classification algorithm for remote sensing images is proposed to address the issue of mixed noise. First, the images are subjected to strong and weak enhancement transformations, and the two types of enhanced images are fed into two networks with the same structure for collaborative learning. Second, by constraining the consistency within two images and the structural consistency between images and corresponding labels during the training process and then combining the two constraints with Binary Cross Entropy (BCE) loss, the final loss function is formed. Finally, based on the prediction labels, the sorting error is defined to identify and correct noisy labels in the loss function, thereby improving the robustness of the model. To verify the performance of the proposed method, mixed noise multi-label classification experiments are conducted on the remote sensing image multi-label datasets AID, UCM, and DFC15. The proposed method is compared with various multi-label classification methods in terms of image classification indicators and multi-label classification indicators. The results indicate that the overall performance of the proposed method is optimal under different label-to-noise ratios.

Key words: remote sense images, multi-label classification, mixed noise, co-learning, label correction