Recently, remote sensing images have become increasingly popular in a number of tasks, such as environmental monitoring. However, the observed images from satellite sensors often suffer from low-resolution (LR), making it difficult to meet the requirements for further analysis. Super-resolution (SR) aims to increase the image resolution while providing finer spatial details, which perfectly remedies the weakness of satellite images. Therefore, in this article, we propose an innovative mixed high-order attention network (MHAN) for remote sensing SR. It comprises two components: a feature extraction network for feature extraction, and a feature refinement network with high-order attention (HOA) mechanism for detail restoration. In the feature extraction network, we replace the elementwise addition with weighted channelwise concatenation in all skip connections, which greatly facilitates the information flow. In the feature refinement network, rather than exploring the first-order statistics (spatial or channel attention), we introduce the HOA module to restore the missing details. Finally, to fully exploit hierarchical features, we introduce the frequency-aware connection to bridge the feature extraction and feature refinement networks. Experiments on two widely used remote sensing image data sets demonstrate that our MHAN not only obtains better accuracy than the state-of-the-art methods but also shows the superiority in terms of running time and GPU cost. Code is available at https://github.com/ZhangDY827/MHAN.

Anomaly detection has a wide range of applications in security area such as network monitoring and smart city/campus construction. It has become an active research issue of great concern in recent years. However, most algorithms of the existing studies are powerless for large-scale and high-dimensional data, and the intermediate data extracted by some methods that can handle high-dimensional data will consume lots of storage space. In this paper, we propose a novel sparse representation framework that learns dictionaries based on the latent space of variational auto-encoder. For large-scale data sets, it can play the role of dimensionality reduction to obtain hidden information, and extract more high-level features than hand-crafted features. At the same time, for the storage of normal information, the space cost can be greatly reduced. To verify the versatility and performance of the proposed learning algorithm, we have experimented on different types of anomaly detection tasks, including KDD-CUP data set for network intrusion detection, Mnist data set for image anomaly detection, and UCSD pedestrian's data set for abnormal event detection in surveillance videos. The experimental results demonstrate that the proposed algorithm outperforms competing algorithms in all kinds of anomaly detection tasks.

The goal of cross-view image matching based on geo-localization is to determine the location of a given ground-view image (front view) by matching it with a group of satellite-view images (vertical view) with geographic tags. Due to the rapid development of unmanned aerial vehicle (UAV) technology in recent years, it has provided a real viewpoint close to 45 degrees (oblique view) to bridge the visual gap between views. However, existing methods ignore the direct geometric space correspondence of UAV-satellite views, and only use brute force for feature matching, leading to inferior performance. In this context, we propose an end-to-end cross-view matching method that integrates cross-view synthesis module and geo-localization module, which fully considers the spatial correspondence of UAV-satellite views and the surrounding area information. To be specific, the cross-view synthesis module includes two parts: the oblique view of UAV is first converted to the vertical view by perspective projection transformation (PPT), which makes the UAV image closer to the satellite image; then we use conditional generative adversarial nets (CGAN) to synthesize the UAV image with vertical view style, which is close to the real satellite image by learning the converted UAV as the input image and the real satellite image as the label. Geo-localization module refers to existing local pattern network (LPN), which explicitly considers the surrounding environment of the target building. These modules are integrated in a single architecture called PCL, which mutually reinforce each other. Our method is superior to the existing UAV-satellite cross-view methods, which improves by about 5%.