Robust Deep Neural Networks for Road Extraction From Remote Sensing Images

Abstract

The application of deep neural networks (DNNs) for road extraction from remote sensing images has gained broad interest because of the competence concerning complex nonlinear relations; however, the presence of noisy labels in the training data sets adversely affects the performance of DNNs. The existing methods of improving the robustness of DNNs focus on modeling the noise distribution. However, these approaches are not satisfactory because of the inaccurate high-level image features obtained by the DNNs. To address this issue, we develop a noise probabilistic model for learning the label noise based on the relationship between the input images, noisy labels, and true labels. The key idea of the probabilistic model is to directly explore the information from the input images and apply it to model the label noise. Then, a robust deep neural network (RDNN) is proposed to instantiate the noise probabilistic model, which consists of two important modules: the true label predictor (TLP) and the noise label estimator (NLE). Especially, the TLP is made of a DNN with softmax, which is used to learn the true label distribution. The NLE is applied to model the label noise distribution, which aims to absorb the label noise in the training process. Moreover, to tackle the challenges in the optimization, we deduce a loss function with the novel regularization, which allows the RDNN to conduct effective training on the noise data set. The effectiveness of the proposed method is validated by experiments on three road data sets that contain various resolutions and imaging conditions. The results demonstrate its superiority over state-of-the-art methods in visual performance and classification accuracy.