An End-to-End Neural Network for Road Extraction From Remote Sensing Imagery by Multiple Feature Pyramid Network

Unlike single geospatial objects extraction from high-resolution remote sensing images, the task of road extraction faces more challenges, including its narrowness, sparsity, diversity, multiscale characteristics, and class imbalance. Focusing on these challenges, this paper proposes an end-to-end framework called the multiple feature pyramid network (MFPN). In MFPN, we design an effective feature pyramid and a tailored pyramid pooling module, taking advantage of multilevel semantic features of high-resolution remote sensing images. In the optimization stage, a weighted balance loss function is presented to solve the class imbalance problem caused by the sparseness of roads. The proposed novel loss function is more sensitive to the misclassified and the sparse real labeled pixels and helps to focus on the spare set of hard pixels in the training stage. Compared with the cross-entropy loss function, the weighted balance loss can reduce training time dramatically for the same precision. Experiments on two challenging datasets of high-resolution remote sensing images which illustrate the performance of the proposed algorithm have achieved significant improvements, especially for narrow rural roads.