Road graph extraction from remote sensing images aims at extracting topological maps composed of road vertices and edges, which has broad prospects in urban planning, traffic management and other applications. However, existing methods are easily affected by complex remote sensing scenes, and also have shortcomings such as poor continuity and slow processing speed. In this paper, we propose a novel end-to-end road extraction method named “Road2Graph”, which encodes road graphs into topological representations for prediction1. We proposed a transformer-based model to encode the deep convolutional features, and then fuse them with the output of the feature extractor to make the network pay more attention to the global multiscale road topology context. We also design an efficient topological representation that encodes attributes such as road segmentation, midpoint map, vertex map, and connection relationships with few parameters and low redundancy. The obtained topological representation can be decoded to obtain the road extraction result in graph format. We conduct experiments on two public datasets - CityScale dataset and SpaceNet dataset. The results show that our method achieves the state-of-art and improves both accuracy (TOPO-F1 +1.55% on CityScale dataset and +2.23% on SpaceNet dataset) and continuity (APLS +7.03% on CityScale dataset and +3.05% on SpaceNet dataset) compared to the other methods.

Road maps are widely used in traffic management, vehicle navigation, urban planning, and other fields. However, automatically extracting road graphs from remote sensing images is very challenging due to the interference of vegetation and buildings and the problem of data imbalance. In this article, we propose a novel end-to-end road graph extraction method for remote sensing images named “TopoRoad,” which learns vectorized representations of road maps guided by topological graphs of the roads. Our method decouples road graph extraction into the predictions of a vertex/degree map (DM), an orientation map, and a segmentation map, which are then jointly decoded to obtain the vertices and edges of the final road graphs. In the vertex branch (VB), we predict the probability map of the vertices for the subsequent vertex extraction. At the same time, to learn local topological connections, the number of connections for each vertex is predicted. For the orientation branch (OB), the connectivity between vertices is obtained by learning to predict the local extension direction of all road pixels. We conduct experiments on two public road extraction datasets—the CityScale dataset and the SpaceNet dataset. The result suggests that our method can produce accurate and continuous road graph extraction results with vectorized representations. Our method achieves state-of-the-art (SOTA) results compared to the other methods in terms of both local and global topological accuracy.

Self-supervised learning (SSL) has recently been introduced to remote sensing (RS) to learn in-domain transferable representations. Here, we propose a semantic decoupled representation learning for RS image change detection (CD). Typically, the object of interest (e.g., building) is relatively small compared to the vast background. Different from existing methods expressing an image into one representation vector that may be dominated by irrelevant land-covers, we disentangle representations of different semantic regions by leveraging the semantic mask. We additionally force the model to distinguish different semantic representations, which benefits the recognition of objects of interest in the downstream CD task. We construct a dataset of bitemporal images with semantic masks in an effort-less manner for pre-training. Experiments on two CD datasets show our model outperforms ImageNet, indomain supervised pre-training, and several recent SSL methods.

Generating hyperspectral images (HSI) from RGB imagery can obtain HSI with both high spatial and spectral resolution, which overcomes the limitations of imaging hardware conditions. Many HSI generation methods target learning a 3-n mapping from RGB to HSI, lacking concern of the spectral categories and spatial distribution. In this paper, we propose an HSI generation method preserving the band structure similarity and semantic information. We design an MLP based classifier and trained it on many spectra of known semantic categories. Then we use it to map the spectra to semantic space and constrain the distance between the embedding of generated spectra and that of the real ones. Meanwhile, a structure similarity loss is added to constrain the spatial information. Experiment results verified the superiority of the proposed method.

In deep learning based road extraction from remote sensing images, the network often learns some features that are not essential to road discrimination, such as trees, buildings, etc. In fact, there is no causal relationship between these features and road discrimination, which will lead to error and omission in final results. In this paper, we propose a novel road extraction network to enhance essential features, including local and global line features and geometric features along the road direction. Multi-scale Line Enhancement Module utilize hough transform to enhance line featues of different scales. Neighboring road prediction branch make the network pre-dict the distance and direction of each pixel to the neighboring road, which helps the network to focus on geometric features along the road direction. Experimental results on the deepglobe dataset show that the network is able to obtain bet-ter road extraction results by enhancing essential features that have a causal relationship with the task. Codes are available at https://github.com/zaoyifan/EssentialFeatures.

Road detection in remote sensing images has been an important research topic in the past few decades. However, with complex backgrounds and occlusion of vehicles and trees, it is difficult for most road detection methods to obtain complete and accurate results. There will be a large number of error and omission detections in such complex scenes due to the poor utilization of detailed information. Therefore, in this article, we propose a novel road detection method called Richer U-Net, which alleviates this problem by designing two detail enhancement strategies. First, considering that convolution operation will cause the loss of detailed information in the feature map, an enhanced detail recovery structure (EDRS) is introduced to make full use of those lost information. It combines the output of each convolutional layer at the same level for the detail recovery of decoding network, leading to more accurate segmentation results. Second, an edge-focused loss function is proposed to guide the network to pay more attention to the road edge area. By adding an enhancement factor, the pixels closer to edge will contribute more loss. The corresponding experiments are conducted on two public datasets, and it can be shown that our method effectively improves final detection results.