Label information plays an important role in supervised high-resolution remote sensing (HRRS) image scene classification. However, the labels of a dataset are probably unreliable and may contain “noisy” labels. Focusing on uncertain labels problem, a covariance matrix representation-based noisy label model (CMR-NLD) is designed for HRRS image scene classification. The main steps are as follows. First, a pretrained convolutional neural network model is employed to extract the scene images deep features and a principal component analysis based dimensionality reduction method is applied to the first fully connected layer to reduce the computational complexity. Then, the noisy training set is constructed by randomly selecting samples into a specific class from other classes samples. We use this set to simulate the actual situation of tag noise to simulate the actual situation of label noise. Second, the covariance between noisy training samples is calculated to obtain the corresponding covariance matrix, and the average value of the obtained covariance matrix is calculated by rows. As a feature of the matrix form, it can both enlarge the subtle differences between different classes and reduce the visual differences of scene images from the same semantic classes. Then, a decision threshold is set to realize the detection and removal of noisy labels. Finally, the improved training sample set will be evaluated by a support vector machine classifier to demonstrate the proposed detector's effectiveness. Experimental results indicate that the proposed method indeed shows great improvement in noisy label detection of HRRS image scene classification.

中文翻译：

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用于遥感图像场景分类的错误标记训练样本的鲁棒学习

标签信息在有监督的高分辨率遥感 (HRRS) 图像场景分类中起着重要作用。但是，数据集的标签可能不可靠，并且可能包含“嘈杂”标签。针对不确定标签问题，设计了一种基于协方差矩阵表示的噪声标签模型（CMR-NLD）用于 HRRS 图像场景分类。主要步骤如下。首先，采用预训练的卷积神经网络模型提取场景图像的深层特征，并将基于主成分分析的降维方法应用于第一全连接层以降低计算复杂度。然后，通过从其他类样本中随机选择样本到特定类中来构建噪声训练集。我们用这个集合来模拟标签噪声的实际情况来模拟标签噪声的实际情况。其次，计算噪声训练样本之间的协方差，得到对应的协方差矩阵，按行计算得到的协方差矩阵的平均值。作为矩阵形式的一个特征，它既可以放大不同类别之间的细微差异，又可以减少来自相同语义类别的场景图像的视觉差异。然后，设置决策阈值以实现噪声标签的检测和去除。最后，改进的训练样本集将通过支持向量机分类器进行评估，以证明所提出的检测器的有效性。