MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images

Abstract

Automatic extraction of the road surface and road centerline from very high-resolution (VHR) remote sensing images has always been a challenging task in the field of feature extraction. Most existing road datasets are based on data with simple and clear backgrounds under ideal conditions, such as images derived from Google Earth. Therefore, the studies on road surface extraction and road centerline extraction under complex scenes are insufficient. Meanwhile, most existing efforts addressed these two tasks separately, without considering the possible joint extraction of road surface and centerline. With the introduction of multitask convolutional neural network models, it is possible to carry out these two tasks simultaneously by facilitating information sharing within a multitask deep learning model. In this study, we first design a challenging dataset using remote sensing images from the GF-2 satellite. The dataset contains complex road scenes with manually annotated images. We then propose a two-task and end-to-end convolution neural network, termed Multitask Road-related Extraction Network (MRENet), for road surface extraction and road centerline extraction. We take features extracted from the road as the condition of centerline extraction, and the information transmission and parameter sharing between the two tasks compensate for the potential problem of insufficient road centerline samples. In the network design, we use atrous convolutions and a pyramid scene parsing pooling module (PSP pooling), aiming to expand the network receptive field, integrate multilevel features, and obtain more abundant information. In addition, we use a weighted binary cross-entropy function to alleviate the background imbalance problem. Experimental results show that the proposed algorithm outperforms several comparative methods in the aspects of classification precision and visual interpretation.

MGML: Multigranularity Multilevel Feature Ensemble Network for Remote Sensing Scene Classification

“…To address this issue, [47] applies the "prediction module" with atrous convolution blocks to extract more abundant global features and uses the "residual refinement module" to correct the residual between the results of the prediction module and the real results. Shao et al [48] adopted atrous convolution blocks and PSP pooling module to integrate multiscale features with large receptive field. Moreover, to tackle the interclass similarity issue and large intraclass variance issue, second-order information is efficiently applied in the RS scene classification task [32], [49], which receives excellent performance.…”

A Model-Driven-to-Sample-Driven Method for Rural Road Extraction

“…(1) The results can be vectorized into a library. Deep learning models require a large number of prior sample sets [43,44], which is prohibitively expensive and laborious. In addition, the road extraction results of deep learning methods lack topological network information, and the results still need extensive intervention before the data can be stored in a database [31,45].…”

A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT

“…Deep learning algorithms are often used for remote sensing applications [10]. Neural networks are implemented using satellite data for detect farms or to classify lands as in [11][12][13][14]. This technique appears to be really convenient in the remote sensing field.…”