The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications. Although deep learning methods have greatly enhanced the development of road extractions in recent years, this technology is still in its infancy. Because the characteristics of road targets are complex, the accuracy of road extractions is still limited. In addition, the ambiguous prediction of semantic segmentation methods also makes the road extraction result blurry. In this study, we improved the performance of the road extraction network by integrating atrous spatial pyramid pooling (ASPP) with an Encoder-Decoder network. The proposed approach takes advantage of ASPP’s ability to extract multiscale features and the Encoder-Decoder network’s ability to extract detailed features. Therefore, it can achieve accurate and detailed road extraction results. For the first time, we utilized the structural similarity (SSIM) as a loss function for road extraction. Therefore, the ambiguous predictions in the extraction results can be removed, and the image quality of the extracted roads can be improved. The experimental results using the Massachusetts Road dataset show that our method achieves an F1-score of 83.5% and an SSIM of 0.893. Compared with the normal U-net, our method improves the F1-score by 2.6% and the SSIM by 0.18. Therefore, it is demonstrated that the proposed approach can extract roads from remote sensing images more effectively and clearly than the other compared methods.

Reliable channel estimation and effective interference cancellation are essential for enhancing the performance of multiple-input-multiple-output (MIMO) underwater acoustic communication (UAC) systems. In this paper, an efficient user-parameter-free Bayesian approach, referred to as sparse learning via iterative minimization (SLIM), is presented. SLIM provides good channel estimation performance along with reduced computational complexity compared to iterative adaptive approach (IAA). Moreover, RELAX-BLAST, which is a linear minimum mean-squared error (MMSE)-based symbol detection scheme, is implemented efficiently by making use of the conjugate gradient (CG) method and diagonalization properties of circulant matrices. The proposed algorithm requires only simple fast Fourier transform (FFT) operations and facilitates parallel implementations. These MIMO UAC techniques are evaluated using both simulated and in-water experimental examples. The 2008 Surface Processes and Acoustic Communications Experiment (SPACE08) experimental results show that the proposed MIMO UAC schemes can enjoy almost error-free performance even under severe ocean environments.

The city of Xi’an, China houses a vast collection of valuable Tang dynasty (618 AD-907 AD) tomb murals that have experienced various degrees of damage over time due to weathering. This research proposes an adversarial edge learning-based mural restoration technique that provides a fast and accurate automatic repair of the murals. The method uses generative adversarial networks to learn how to repair the edge contour of a damaged mural and restore the missing content. The contour restoration network and mural restoration networks are trained and updated simultaneously to enhance the feature extraction and restoration coordination performance of the network. The experiments were conducted on a self-built Tang dynasty tomb mural painting restoration dataset and compared to several existing algorithms. The proposed algorithm showed a 1.3 increase in peak signal-to-noise ratio (PSNR) and a 1.43% increase in structural similarity (SSIM) value, providing a precise restoration of the structure and contour of the mural content. The algorithm successfully repaired damaged murals with fractures and small missing parts. The suggested method can be useful for both the technical repair and conservation of significant national visual cultural property and the digital restoration of historic murals.

In this article, a road-network-based geolocalization method is proposed. We match roads in the onboard images to the reference road vector map, and realize successful localization over areas as large as a whole city. The road network matching problem is treated as a point cloud registration problem under the homography transformation and solved under the hypothesize-and-test framework. To tackle the point cloud registration problem, a global projective-invariant feature is proposed, which consists of two road intersections augmented with their tangents. In addition, we propose the necessary conditions for the features to match. This can reduce the candidate matching features, thus accelerating the search to a great extent. These matching candidates are first ``filtered'' with the model consistency check in parameter space and then tested with similarity metrics to identify the correct transformation. The experiments show that our method can localize an aerial image over an area larger than 1000 km² within several seconds on a single CPU. Our code can be found at: https://github.com/FlyAlCode/RCLGeolocalization-2.0.

Land cover segmentation is an important and challenging task in the field of remote sensing. Even though convolutional neural networks (CNNs) provide great support for semantic segmentation, standard models are still difficult to capture global information and long-range dependencies in remote sensing images. To overcome these limitations, we proposed an attention guided encoder-decoder network with multi-scale context aggregation to achieve more accurate segmentation of land cover. Based on the structure of the encoder-decoder network, we introduce a multi-scale feature fusion module with two attention modules to the top of the encoder. The multi-scale feature fusion module is employed to aggregate multi-scale features and capture global correlations. The attention modules are used to exploit the long-range dependencies and the interdependence between channels from the perspective of space and channel respectively. The experimental results on the GF-2 images show that our proposed method achieves state-of-the-art performance, with an OA of 84.1% and the mIoU of 62.3%. Compared with the baseline network, our method improves the OA by 3.3% and the mIoU by 4.4%. The comparative experiments also demonstrate that the proposed approach can significantly improve the accuracy of land cover segmentation than other compared methods.

Purpose: The purpose of this paper is to study the road segmentation problem of cross-modal remote sensing images. Design/methodology/approach: First, the baseline network based on the U-net is trained under a large-scale dataset of remote sensing imagery. Then, the cross-modal training data are used to fine-tune the first two convolutional layers of the pre-trained network to achieve the adaptation to the local features of the cross-modal data. For the cross-modal data of different band, an autoencoder is designed to achieve data conversion and local feature extraction. Findings: The experimental results show the effectiveness and practicability of the proposed method. Compared with the ordinary method, the proposed method gets much better metrics. Originality/value: The originality is the transfer learning strategy that fine-tunes the low-level layers for the cross-modal data application. The proposed method can achieve satisfied road segmentation with a small amount of cross-modal training data, so that is has a good application value. Still, for the similar application of cross-modal data, the idea provided by this paper is helpful.