Urban road extraction has wide applications in public transportation systems and unmanned vehicle navigation. The high-resolution remote sensing images contain background clutter and the roads have large appearance differences and complex connectivities, which makes it a very challenging task for road extraction. In this article, we propose a novel end-to-end deep learning model for road area extraction from remote sensing images. Road features are learned from three levels, which can remove the distraction of the background and enhance feature representation. A direction-aware attention block is introduced to the deep learning model for keeping road topologies. We compare our method on public remote sensing data sets with other related methods. The experimental results show the superiority of our method in terms of road extraction and connectivity preservation.

Image hashing has been widely used in image retrieval tasks. Many existing methods generate hashing codes based on image feature representations. They rarely consider the rich information such as image clustering information contained in the image set as well as uncertain relationships between images and tags simultaneously. In this paper, we develop a Weighted Generative Adversarial Networks (WeGAN) to transfer the clustering information of images to construct the hashing code. WeGAN consists three modules: 1) a hashing learning process for transferring knowledge of the image set to hashing codes of single images; 2) by means of hashing codes, a module to generate image content, tag representation, and their joint information which reflects the correlation between the image and the corresponding tags; 3) a discriminator to distinguish the generated data from the original source, and then formulating three loss functions. Different weights are assigned to these loss functions in order to deal with the uncertainties between images and tags. Through introducing the image set to process the image hashing with different tags, WeGAN can naturally provide the information of clustering results, which is useful for self-supervision of image hashing with multi-tags. The generated hashing code has the ability to dynamically process the uncertain relationships between images and tags. Experiments on three challenging datasets show that WeGAN outperforms the state-of-the-art methods.

Due to the large intraclass variances and complicated object distribution, recognizing objects with complex appearances and arbitrary orientations has been an active research topic and a challenging task in remote sensing fields. In this article, we formulate object recognition as a high-level feature-learning problem, and a novel supervised method is proposed to learn high-level feature representations from high-resolution remote sensing images for object recognition. Our method simultaneously and coherently achieves high-level feature learning and classifier training, which improves the recognition performance. Two constraints that enforce the label consistencies of group images and label consistencies of single images are introduced in a deep learning framework to obtain the high-level feature space. The high-level feature and a multiclass linear classifier are finally learned by an effective optimization algorithm. Experimental results demonstrate the superior performance of the proposed method over many state-of-the-art techniques in object recognition.

Road network extraction from satellite images is widely applicated in intelligent traffic management and autonomous driving fields. The high-resolution remote sensing images contain complex road areas and distracted background, which make it a challenge for road extraction. In this study, we present a stacked multitask network for end-to-end segmenting roads while preserving connectivity correctness. In the network, a global-aware module is introduced to enhance pixel-level road feature representation and eliminate background distraction from overhead images; a road-direction-related connectivity task is added to ensure that the network preserves the graph-level relationships of the road segments. We also develop a stacked multihead structure to jointly learn and effectively utilize the mutual information between connectivity learning and segmentation learning. We evaluate the performance of the proposed network on three public remote sensing datasets. The experimental results demonstrate that the network outperforms the state-of-the-art methods in terms of road segmentation accuracy and connectivity maintenance.