The YOLO network has been extensively employed in the field of ship detection in optical images. However, the YOLO model rarely considers the global and local relationships in the input image, which limits the final target prediction performance to a certain extent, especially for small ship targets. To address this problem, we propose a novel small ship detection method, which improves the detection accuracy compared with the YOLO-based network architecture and does not increase the amount of computation significantly. Specifically, attention mechanisms in spatial and channel dimensions are proposed to adaptively assign the importance of features in different scales. Moreover, in order to improve the training efficiency and detection accuracy, a new loss function is employed to constrain the detection step, which enables the detector to learn the shape of the ship target more efficiently. The experimental results on a public and high-quality ship dataset indicate that our method realizes state-of-the-art performance in comparison with several widely used advanced approaches.

Remote sensing target detection technology in cloud and mist scenes is of great significance for applications such as marine safety monitoring and airport traffic management. However, the degradation and loss of features caused by the obstruction of cloud and mist elements still pose a challenging problem for this technology. To enhance object detection performance in adverse weather conditions, we propose a novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions. Specifically, a component-decoupling-based background suppression (CDBS) module is proposed, which extracts cloud and mist components based on characteristic priors and effectively enhances the contrast between the target and the environmental background through a background subtraction strategy. Moreover, a local-global semantic joint mining (LGSJM) module is utilized, which combines convolutional neural networks (CNNs) and hierarchical selective attention to comprehensively mine global and local semantics, achieving target feature enhancement. Finally, the experimental results on multiple public datasets indicate that the proposed method realizes state-of-the-art performance compared to six advanced detectors, with mAP, precision, and recall indicators reaching 85.5%, 89.4%, and 77.9%, respectively.

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.

Hyperspectral imaging (HSI) data pose both opportunities and challenges for target detection due to the high spectral resolution and vast data volume. Traditional band selection methods for HSI often prioritize image quality or information content, neglecting target distinctiveness in specific detection tasks. To address this issue, this work proposes a novel band selection method, genetic algorithm-based weighted constraint target band selection (GA-WCTBS), which utilizes an improved genetic algorithm to optimize band subsets for small target detection. GA-WCTBS prioritizes target distinctiveness and background clutter fluctuations by a proposed spectral signal-to-clutter ratio (SCR) inspired by the constraint target method, even in bands with lower image quality. It employs a genetic algorithm to consider the combinatorial potential of bands for optimal detection. Additionally, a k-means and weight assignment strategy improves the background estimation for selecting a band subset with better clutter suppression capability. Experiments on widely used public ABU and AVIRIS datasets demonstrate that the band subset selected by GA-WCTBS significantly outperforms the existing methods in terms of detection capability.

The vehicle detection in remote sensing images is a challenging task due to the small size of the objects and interference of a complex background. Traditional methods require a large number of anchor boxes, and the intersection rate between these anchor boxes and an object’s real position boxes needs to be high enough. Moreover, the size and aspect ratio of each anchor box need to be designed manually. For small objects, more anchor boxes need to be set. To solve these problems, we regard the small object as a keypoint in the relevant background and propose an anchor-free vehicle detection network (AVD-kpNet) to robustly detect small-sized vehicles in remote sensing images. The AVD-kpNet framework fuses features across layers with a deep layer aggregation architecture, preserving the fine features of small objects. First, considering the correlation between the object and the surrounding background, a 2D Gaussian distribution strategy is adopted to describe the ground truth, instead of a hard label approach. Moreover, we redesign the corresponding focus loss function. Experimental results demonstrate that our method has a higher accuracy for the small-sized vehicle detection task in remote sensing images compared with several advanced methods.

Remote sensing image change detection is a valuable technology for analyzing the earth observation data. It has significant application value in resource monitoring, disaster assessment and urban planning. However, current change detection methods have not fully explored the interrelationships between bi-temporal data, and the extraction process of change information lacks prior guidance and constraints. Therefore, it is easy to produce missed detections and false alarms when facing complex backgrounds and variable objects in remote sensing images. To tackle such issues, we propose a cross-scale guided high-order feature interaction change detection network for dual temporal images. Specifically, a cross-scale guided dual encoderdecoder backbone is proposed to constrain the reconstruction process of change objectives, and guide geometric prior to optimize the representation of target structures. Next, an efficient high-order feature interaction module is designed, employing multi-level receptive fields to enhance the perception ability for multiscale features. Moreover, we construct a bi-temporal feature alignment fusion module, which decouples and filters out the interference of background pseudo changes through interactive perception of spatial-temporal differences. Comprehensive experimental validation is undertaken on four representative change detection datasets (LEVIR-CD, WHU-CD, DSIFN-CD and S2Looking). The findings demonstrate that the network demonstrated state-of-the-art performance.