Anomaly detection (AD) has emerged as a critical area of research in hyperspectral imagery (HIS) processing, focusing on detecting sparse, small targets with spectral and spatial features deviating from the background without prior information. The approach of AD based on reconstruction differences is a leading method in deep learning (DL) for hyperspectral AD (HAD). A key challenge is the accurate estimation of complex backgrounds. The essence of this challenge lies in accurately reconstructing 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 HAD. 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 areas within the neighborhood on background estimation. Moreover, the scarcity of paired labeled HSIs 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 the principal component analysis (PCA) and the linear spectral mixing model (LSMM). 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 (SOTA) HAD methods across six different areas under the curve (AUC) metrics.

Detecting aircraft in complex remote sensing scene has significant value for military and civilian applications. To overcome the interference of complex environmental factors and achieve high-accuracy detection performance, the comprehensive utilization of optical and SAR images for object detection has become a promising research direction. However, currently there are problems with optical and SAR fusion detection, such as difficulty in obtaining paired registration training data and incomplete consideration of feature elements in fusion model. To tackle these challenges, we present an aircraft detection method based on optical-SAR complementarity-aware feature fusion. Firstly, an unpaired image translation model based on scattering feature enhancement GAN (SFEG) is designed to generate SAR images that are pixel-level registered with the input optical image. On this basis, a complementarity-aware feature fusion detection network (CFFDNet) combining differential feature spatial-aware complementary (DFSC) units and gate-generated weighted fusion (GWF) units is proposed to enhance the effective features of single source image while improving the complementary fusion effect of multimodal features. Experiments on CORS-ADD and MAR20 datasets demonstrate that our method outperforms the compared classical single-modal and multimodal detection models. The latest code is available soon at: https://github.com/JimmyRSlab/Complementarity-aware-Feature-Fusion-for-Aircraft-Detection-via-Unpaired-Opt2SAR-Image-Translation.

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% [email protected], and 1.6% [email protected]:0.95 in the proposed dataset.

Infrared small target detection (IRSTD) 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 IRSTD network, named frequency domain decoupling bidirectional interactive attention network (FDDBA-NET), designed from the perspective of frequency domain decoupling (FDD). 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. In addition, 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.

In temporal infrared small target detection, it is crucial to leverage the disparities in spatiotemporal 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 spatiotemporal characteristics, making target detection highly challenging. To address these challenges, we propose a network named LMAFormer. First, we introduce a local motion-aware spatiotemporal attention mechanism that aligns and enhances multiframe features to extract local spatiotemporal salient features of targets while avoiding interference from moving backgrounds. Second, we employ a multiscale fusion transformer encoder that computes self-attention weights across and within scales during encoding, to establish multiscale correlations among different regions of temporal images, enabling motion background modeling. Last, we propose a multiframe joint query decoder. The shallowest feature map after multiscale 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 multiframe 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 athttps://github.com/lifier/LMAFormer.

Thin-film diffractive optical elements (DOEs) have considerable potential to be used in the field of high-resolution remote sensing imaging satellites because of advantages such as a large aperture, small volume, lightness, wide tolerance range of surface shape, and easy replication. However, there are problems associated with thin-film diffraction imaging, including space variation, serious blur, and low contrast, which result in insufficient imaging quality with regard to traditional optical system requirements. To address this, a local adaptive prior-based image restoration method is proposed for thin-film diffraction imaging systems. An entire degraded image was divided into several isohalo regions based on imaging characteristics. Then, the regularization constraints were adaptively selected and updated according to the local scene prior characteristics. Additionally, the system parameters in the corresponding field of view were used as input to restore each subregion. In particular, the diffraction efficiency (DIE) was introduced into the model to remove the nondesign level background radiation. The experimental results show that the proposed algorithm can effectively improve the image quality of a thin-film diffraction imaging system, including space variation correction, clarity enhancement, and background radiation suppression. Furthermore, a DIE of less than 60% was found to significantly impact the final image products.

Small target detection in remote sensing images has considerable significance in practical applications such as military dynamic discrimination and traffic monitoring. However, the limited appearance features of small-scale targets and the widespread false alarm sources make small target detection in remote sensing images a tough challenge. To address these problems, we propose a novel small detection method by employing an adaptive multi-level feature fusion module (AMFFM) and an attention-augmented high-resolution head (AAHRH). Specifically, AMFFM is designed to suppress the interference of false alarm sources in complicated scenes. We upsample the high-level features by the context modeling of semantic information and refine the low-level features for noise removal. Then the enhanced multi-level features are fused based on the spatial and channel significance. After that, AAHRH is put forward to enhance the perception of small targets by embedding cross-dimension interaction with the attention mechanism. The prediction heads are reconstructed with high-resolution layers to improve the detection performance in densely distributed scenes. We conduct dilated and comparison experiments on a constructed small car dataset, a public small ship dataset, and the VEDAI dataset. The experimental results on two datasets verify the effectiveness and robustness of the proposed method with the state-of-the-art performance.

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.

The infrared (IR) small target detection algorithm with a high detection rate, low false alarm rate, and high real-time performance has significant application value in the field of IR remote sensing. IR small targets in complex backgrounds have low contrast and low signal-to-noise ratio (SNR). Therefore, small target detection is more difficult. Traditional IR small target detection is generally implemented by local contrast methods (LCM), nonlocal autocorrelation methods (NAM), and adaptive segmentation. In this letter, a robust infrared small target detection network (RISTDnet) is proposed based on deep learning. In RISTDnet, a feature extraction framework combining handcrafted feature methods and convolutional neural networks is constructed, a mapping network between feature maps and the likelihood of small targets in the image is established, and a threshold is applied on the likelihood map to segment real targets. Experimental results show that the RISTDnet can detect small targets with different sizes and low SNRs in complex backgrounds and have better effectiveness and robustness against existing algorithms.

With the continuous increase in the resolution of optical remote sensing satellites, the influence of space variations on the image quality cannot be ignored, especially in new imaging systems such as thin-film diffraction and rectangular rotating pupils. This paper was conducted to analyze the influence of space variability on restoration performances of different methods, then a new processing strategy of space-variant images is proposed. According to the analytical experiment results, we suggest using the block method when the PSV < 0.20% and otherwise selecting the global method. In order to ensure the final image quality, we also suggest controlling the PSV within 0.28% when designing optical systems. This study can provide a foundation for optimizing the design of front-end optical systems and selecting back-end processing methods in engineering applications.

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 utilized to aggregate context information from both 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.

Diffractive membrane imaging systems have been an important development trend for high-orbit satellite cameras owing to their advantages of large aperture, light weight, rapid manufacture, and low cost. However, caused by the cross-coupling effects of diffraction imaging, membrane properties, subaperture stitching, on-orbit disturbances, and other physical factors, lager-aperture space diffractive membrane imaging systems have specific and complex degradation characteristics: the modulation transfer function (MTF) and signal-to-noise ratio (SNR) have more prominent degradation and serious space-variant characteristics over fields of view, with obvious background radiation properties that seriously affect the application of imaging products. To address this problem, this study established a global information transmission model by characterizing the PSF and background radiation in a full field of view to represent the imaging law of an on-orbit system. Aiming at the inverse problem of the information transmission model, we also propose a novel image inversion restoration method for the special degradation characteristics. In particular, the effect of diffraction efficiency is introduced into the inversion restoration method to solve the background radiation problem. Moreover, we innovatively designed matrix regularization parameters to further improve the correction ability of spatial variation. When the diffraction efficiency was experimentally higher than 60% and the mean measured spatial variability was less than 0.2, the proposed method exhibited a satisfactory processing performance, and could improve multiobjective comprehensive processing, such as transfer function compensation, spatial variation correction, and background radiation removal.