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. To address MLC problems, the use of deep neural networks that require a high number of reliable training images annotated by multiple land-cover class labels (multi-labels) have been found popular in RS. However, collecting such annotations is time-consuming and costly. A common procedure to obtain annotations at zero labeling cost is to rely on thematic products or crowdsourced labels. As a drawback, these procedures come with the risk of label noise that can distort the learning process of the MLC algorithms. In the literature, most label noise robust methods are designed for single label classification (SLC) problems in computer vision (CV), where each image is annotated by a single label. Unlike SLC, label noise in MLC can be associated with: 1) subtractive label-noise (a land cover class label is not assigned to an image while that class is present in the image); 2) additive label-noise (a land cover class label is assigned to an image although that class is not present in the given image); and 3) mixed label-noise (a combination of both). In this paper, we investigate three different noise robust CV SLC methods and adapt them to be robust for multi-label noise scenarios in RS. During experiments we study the effects of different types of multi-label noise and evaluate the adapted methods rigorously. To this end, we also introduce a synthetic multi-label noise injection strategy that is more adequate to simulate operational scenarios compared to the uniform label noise injection strategy, in which the labels of absent and present classes are flipped at uniform probability. Further, we study the relevance of different evaluation metrics in MLC problems under noisy multi-labels.
翻译:开发遥感图像多标签分类(MLC)的准确方法(MLC)是RS公司最重要的研究课题之一。为了解决刚果解放运动的问题,在RS公司中发现使用深神经网络,这需要大量可靠的培训图像,并配有多个土地覆盖类标签(多标签)。然而,收集这种说明既费时又费钱。一种以零标签成本获得注释的常见程序是依赖专题产品或众源标签。作为退步,这些程序伴随着标签噪音的风险,这种噪音可能会扭曲刚果解放运动算法的学习过程。在文献中,大多数标签噪声稳健的方法是为计算机视觉中的单一标签分类(SLC)问题设计的,其中每种图像都有单一标签标签标签标签。与SLC公司不同的是:1)降低标签噪音(一种土地覆盖类标签标签标签标签没有被进一步指定给一个图像,而在图像中存在适当的升级, 标签类标签类标签标签被分配到一个图像,尽管在这种分类中并不具有稳健的内值。 并且标签的多标签在本文中是不同的标签 。