With the increasing demand for the wide-area refined detection of aircraft targets, remote sensing cameras have adopted an ultra-large area-array detector as a new imaging mode to obtain broad width remote sensing images (RSIs) with higher resolution. However, this imaging technology introduces new special image degradation characteristics, especially the weak target energy and the low signal-to-noise ratio (SNR) of the image, which seriously affect the target detection capability. To address the aforementioned issues, we propose an aircraft detection method for RSIs with low SNR, termed L-SNR-YOLO. In particular, the backbone is built blending a swin-transformer and convolutional neural network (CNN), which obtains multiscale global and local RSI information to enhance the algorithm’s robustness. Moreover, we design an effective feature enhancement (EFE) block integrating the concept of nonlocal means filtering to make the aircraft features significant. In addition, we utilize a novel loss function to optimize the detection accuracy. The experimental results demonstrate that our L-SNR-YOLO achieves better detection performance in RSIs than several existing advanced methods.

The growing intelligence and prevalence of drones have led to an increase in their disorderly and illicit usage, posing substantial risks to aviation and public safety. This paper focuses on addressing the issue of drone detection through surveillance cameras. Drone targets in images possess distinctive characteristics, including small size, weak energy, low contrast, and limited and varying features, rendering precise detection a challenging task. To overcome these challenges, we propose a novel detection method that extends the input of YOLOv5s to a continuous sequence of images and inter-frame optical flow, emulating the visual mechanisms employed by humans. By incorporating the image sequence as input, our model can leverage both temporal and spatial information, extracting more features of small and weak targets through the integration of spatiotemporal data. This integration augments the accuracy and robustness of drone detection. Furthermore, the inclusion of optical flow enables the model to directly perceive the motion information of drone targets across consecutive frames, enhancing its ability to extract and utilize features from dynamic objects. Comparative experiments demonstrate that our proposed method of extended input significantly enhances the network’s capability to detect small moving targets, showcasing competitive performance in terms of accuracy and speed. Specifically, our method achieves a final average precision of 86.87%, representing a noteworthy 11.49% improvement over the baseline, and the speed remains above 30 frames per second. Additionally, our approach is adaptable to other detection models with different backbones, providing valuable insights for domains such as Urban Air Mobility and autonomous driving.

Aircraft detection in remote sensing images is significant in both military and civilian fields, such as air traffic control and battlefield dynamic monitoring. Deep learning methods can achieve promising detection performance with sufficient and labeled samples. However, current aircraft datasets are mainly from a single data source and lack diverse scenes and targets, making it difficult to train a robust and generalized detector. Therefore, we manually label and construct a complex optical remote sensing aircraft target detection dataset (CORS-ADD) from Google Earth and multiple satellites such as WorldView-2, WorldView-3, Pleiades, Jilin-1, and IKONOS. It contains 7,337 images covering typical airports and various rare scenes, including the aircraft carrier, ocean and land with flying aircraft. The dataset consists of 32,285 civil and military aircraft instances, including bombers, fighters, and early warning aircraft. These targets range from 4×4 pixels to 240×240 pixels and are all labeled with both horizontal bounding box (HBB) and oriented bounding box (OBB) annotations. The various scenes and sufficient instances can fully support the training and evaluation of data-driven algorithms. Meanwhile, based on the constructed dataset, we train and evaluate several detectors to provide a benchmark and help promote the development of aircraft detection techniques.

Anomaly detection has emerged as a critical area of research in hyperspectral imagery processing, focusing on detect sparse, small targets with spectral and spatial features deviating from the background without prior information. The approach of anomaly detection based on reconstruction differences is a leading method in deep learning for hyperspectral anomaly detection (HAD). A key challenge is the accurate estimation of complex backgrounds. The essence of this challenge lies in accurately reconstruct background regions while inferring the latent background of anomaly regions. In this article, we propose a novel method called dual-window spectral diffusion (DWSDiff) for hyperspectral anomaly detection. To address the challenge of complex background estimation in HSIs, we developed a spectral diffusion model specifically tailored for HSI. This model achieves precise background estimation through an iterative spectral diffusion and reverse reconstruction process. We also introduced a dual-window strategy to mitigate the influence of anomaly extension area within the neighborhood on background estimation. Moreover, the scarcity of paired labeled hyperspectral images from the same scene, with and without anomalies, limits the model’s ability to learn features between anomaly and background. To address the shortage, we devised an anomaly generation strategy based on principal component analysis and the linear spectral mixing model. Building on these, we designed a training and inference framework that integrates spectral diffusion, reverse background reconstruction, and target detection. Experimental results on the Airport–Beach–Urban (ABU) hyperspectral datasets demonstrate that DWSDiff outperforms 20 state-of-the-art HAD methods across 6 different AUC metrics.

Ship detection in optical remote sensing images plays a significant role in a wide range of civilian and military tasks. However, it is still a challenging issue owing to complex environmental interferences and a large variety of target scales and positions. To overcome these limitations, we propose a supervised multi-scale attention-guided detection framework, which can effectively detect ships of different scales both in complex pure ocean and port scenes. Specifically, a multi-scale supervision module is first proposed to adjust the semantic consistency of different feature levels, obtaining extracted features with small semantic gaps. Next, an attention-guided module is used to aggregate context information from both the spatial and channel dimensions by calculating map correlations, adaptively enhancing the feature representation. Moreover, to preserve the attribute and spatial relationship of the optimized features, we adopt a capsule-based module as the classifier and obtain satisfactory classification performance. Experimental results conducted on two public high-quality datasets demonstrate that the proposed method obtains state-of-the-art performance in comparison with several advanced methods.