论文标题
多标签噪声强大的协作学习用于遥感图像分类
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification
论文作者
论文摘要
遥感（RS）图像的多标签分类（MLC）精确方法的开发是RS中最重要的研究主题之一。基于卷积神经网络（CNN）的MLC方法在Rs中显示出强大的性能增长。但是，他们通常需要大量的可靠培训图像，并带有多个土地覆盖类标签。收集此类数据是耗时且昂贵的。为了解决这个问题，可以使用嘈杂标签的公开主题产品可用于注释零标记成本的RS图像。但是，多标签噪声（可能与错误和缺失标签注释相关联）可能会扭曲MLC方法的学习过程。为了解决这个问题，我们提出了一种新型的多标签噪声稳健协作学习（RCML）方法，以减轻CNN模型训练阶段中多标签噪声的负面影响。 RCML根据三个主要模块识别，排名和排除RS图像中的嘈杂多标签：1）差异模块； 2）组拉索模块； 3）交换模块。差异模块可确保两个网络学习各种功能，同时产生相同的预测。组LASSO模块的任务是检测分配给多标记训练图像的潜在嘈杂标签，而交换模块则用于在两个网络之间交换排名信息。与对噪声分布的假设的现有方法不同，我们提出的RCML对训练集中的噪声类型没有任何事先假设。在两个多标签RS图像档案上进行的实验证实了在极端多标签噪声速率下提出的RCML的鲁棒性。我们的代码可在以下网址公开获取：http：//www.noisy-labels-in-rs.org
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on convolutional neural networks (CNNs) have shown strong performance gains in RS. However, they usually require a high number of reliable training images annotated with multiple land-cover class labels. Collecting such data is time-consuming and costly. To address this problem, the publicly available thematic products, which can include noisy labels, can be used to annotate RS images with zero-labeling cost. However, multi-label noise (which can be associated with wrong and missing label annotations) can distort the learning process of the MLC methods. To address this problem, we propose a novel multi-label noise robust collaborative learning (RCML) method to alleviate the negative effects of multi-label noise during the training phase of a CNN model. RCML identifies, ranks and excludes noisy multi-labels in RS images based on three main modules: 1) the discrepancy module; 2) the group lasso module; and 3) the swap module. The discrepancy module ensures that the two networks learn diverse features, while producing the same predictions. The task of the group lasso module is to detect the potentially noisy labels assigned to multi-labeled training images, while the swap module is devoted to exchange the ranking information between two networks. Unlike the existing methods that make assumptions about noise distribution, our proposed RCML does not make any prior assumption about the type of noise in the training set. The experiments conducted on two multi-label RS image archives confirm the robustness of the proposed RCML under extreme multi-label noise rates. Our code is publicly available at: http://www.noisy-labels-in-rs.org
