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.

In temporal infrared small target detection, it is crucial to leverage the disparities in spatio-temporal characteristics between the target and the background to distinguish the former. However, remote imaging and the relative motion between the detection platform and the background cause significant coupling of spatio-temporal characteristics, making target detection highly challenging. To address these challenges, we propose a network named LMAFormer. First, we introduce a local motion-aware spatio-temporal attention mechanism that aligns and enhances multi-frame features to extract local spatio-temporal salient features of targets while avoiding interference from moving backgrounds. Second, we employ a multi-scale fusion transformer encoder that computes self-attention weights across and within scales during encoding, to establish multi-scale correlations among different regions of temporal images, enabling motion background modeling. Lastly, we propose a multi-frame joint query decoder. The shallowest feature map after multi-scale feature propagation is mapped to initial query weights, which are refined through grouped convolutions to generate grouped query vectors. These are jointly optimized to encapsulate rich multi-frame details, strengthening motion background modeling and target feature representation, improving prediction accuracy. Experimental results on the NUDT-MIRSDT, IRDST and the established TSIRMT datasets demonstrate that our network outperforms state-of-the-art (SOTA) methods. Our code and dataset will be available at https://github.com/lifier/LMAFormer.

Infrared small target detection involves determining the coordinate position of the target in complex infrared images. However, challenges arise due to the absence of internal texture structure, edge dispersion, weak energy characteristics of the target, and a significant amount of background clutter resembling the target’s morphology, impeding precise target location. To address these challenges, we propose an infrared small target detection network, named FDDBA-NET (Frequency Domain Decoupling Bidirectional Interactive Attention Network), designed from the perspective of frequency domain decoupling. To suppress backgrounds that are similar in shape and structure to the target, exploiting the spectral differences between the target and background in the frequency domain, we adopt two learnable masks to extract the target-specific spectrum and the target-background-consistent spectrum detrimental to detection. The specific spectrum aids in target detection. A target-level contrast loss is designed to maximize the disparity between these two spectra, ensuring optimal detection results. Additionally, to preserve target details in high-level semantic information, we introduce a bidirectional interactive attention module that leverages mutual modulation of deep global and shallow local features, facilitating deep and shallow feature fusion. To validate our approach, we conduct experiments comparing our proposed network with state-of-the-art conventional methods and deep learning methods on public datasets. The results demonstrate the superior performance of our method.

Aircraft object detection in remote sensing images is a challenging task, especially for small objects, complex backgrounds, and aircraft objects in flight. Due to the lack of contextual relationship between the aircraft object and the surrounding background during flight and the small number of pixels in the object itself, the use of regular rectangular convolution for feature extraction results in many background pixels being sampled. This article proposes YOLO-FRS for aircraft object in flight detection, which uses a new module based on deformable convolution (DCN), the feature response separation deformable convolution (FRS-DCN) module. The FRS-DCN module adds semantic segmentation supervision on the basis of stacked DCNs, so that the background and object in the input of DCNs in this module are separated as much as possible. We design a soft label annotation and loss calculation method for semantic segmentation supervision. In addition, we propose a flight-state aircraft object dataset that includes multiple backgrounds and cloud interference. YOLO-FRS was tested on the proposed dataset, and the results showed that the FRS-DCN module improved the performance of aircraft object detection. Compared with multiple mainstream deep convolution models, the YOLO-FRS model also exhibits competitive performance.

Aircraft target detection is a challenging task in remote sensing images, especially for aircraft small target detection. The most advanced object detection framework currently processes all information in the image uniformly through a deep neural network. In the past, in the process of detecting aircraft small targets, the feature extraction process was carefully designed, and hand-crafted features were derived from expert knowledge or historical data, which included prior knowledge that was conducive to object detection. Embedding prior features into deep neural networks can enhance the saliency of target information, improve the detection performance of the model. Accordingly, this paper proposes a Hand-crafted Feature Fusion Stream (HFFS) for embedding prior knowledge. We obtain hand-crafted features based on the grayscale co-occurrence matrix and edge extraction operator, and generate an attention map in deep convolutional neural networks (CNNs) to achieve the fusion of hand-crafted feature maps and high-level feature maps in deep convolutional networks. The experimental results show that using HFFS on the baseline model improves the detection performance of the model for aircraft small targets. Compared with the baseline model, our detection model achieves improvements of 1.1% AR, 1.6% AP@0.5, and 1.6% AP@0.5:0.95 in the proposed dataset.

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.