A balanced random learning strategy for CNN based Landsat image segmentation under imbalanced and noisy labels

Abstract

Landsat image segmentation is important for obtaining large-scale land cover maps. The accuracy of CNN-based Landsat image segmentation highly depends on the quantity and quality of the training samples. However, enough accurate labels for Landsat images are difficult to access. Fortunately, traditional classifier induced segmentation results can be considered as an alternative, although they are noisy and unbalanced to a certain extent. To resist noisy labels and alleviate the impact of imbalanced samples, this paper proposes a confidence interval based balanced random learning strategy. Firstly, a confidence interval-based mask is employed to control the random learning rate of the network from the entire noisy training set. Then, the multi-layer feature maps of CNN are fully utilized to compensate for the information loss in random learning, in which down-sampled labels are used to decrease the uncertainty brought by up-sampling CNN feature maps. In addition, considering the corruption of noisy labels on different classes, a balanced random learning with different confidence levels is performed on each class to further improve the learning ability of CNN. Experimental results on two widely used backbones, namely VGGNet and ResNet, demonstrate that the proposed balanced random learning strategy can effectively improve the performance of CNN under imbalanced and noisy labels, which can be improved by 3.41%.