Abstract
Deep learning has gained broad interest in remote sensing image scene classification thanks to the effectiveness of deep neural networks in extracting the semantics from complex data. However, deep networks require large amounts of training samples to obtain good generalization capabilities and are sensitive to errors in the training labels. This is a problem in remote sensing since highly reliable labels can be obtained at high costs and in limited amount. However, many sources of less reliable labeled data are available, e.g., obsolete digital maps. In order to train deep networks with larger datasets, we propose both the combination of single or multiple weak sources of labeled data with a small but reliable dataset to generate multisource labeled datasets and a novel training strategy where the reliability of each source is taken in consideration. This is done by exploiting the transition matrices describing the statistics of the errors of each source. The transition matrices are embedded into the labels and used during the training process to weigh each label according to the related source. The proposed method acts as a weighting scheme at gradient level, where each instance contributes with different weights to the optimization of different classes. The effectiveness of the proposed method is validated by experiments on different datasets. The results proved the robustness and capability of leveraging on unreliable source of labels of the proposed method.
Abstract (translated)
深度学习在遥感图像场景分类中因深度神经网络提取复杂数据语义的有效性而引起了广泛兴趣。然而，深度网络需要大量的训练样本以获得良好的泛化能力，并且对训练标签中的错误非常敏感。这在遥感领域是一个问题，因为高度可靠的标签只能在高成本下获取并且数量有限。但是，许多来源的低质量标注数据是可用的，例如过时的数字地图。为了使用更大的数据集来训练深度网络，我们提出了一种方法：将单一或多个弱源的数据与一个小但可靠的数据集结合，生成多源标签数据集，并提出一种新的训练策略，在该策略中考虑了每个来源的可靠性。通过利用描述每个来源错误统计信息的转换矩阵实现了这一点。这些转换矩阵被嵌入到标签中并在训练过程中使用以根据相关来源对每个标签进行加权。所提出的方法在梯度级别上充当加权方案，使得每个实例能够根据不同类别贡献不同的权重来进行优化。通过不同数据集上的实验验证了所提方法的有效性。结果证明了该方法利用不可靠的标签源的鲁棒性和能力。
URL
https://arxiv.org/abs/2510.05760
