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.

Remote sensing image change detection is an important means for Earth monitoring task, which has a wide application prospect. In multitemporal optical remote sensing, there are inherent differences in factors such as lighting and sensors. This leads to the coupling of content change and image style change, making it difficult to distinguish. Therefore, a meaningful thinking for change detection is to decouple and capture the real changes of ground objects from multitemporal images. Based on this motivation, a novel general change detection architecture is explored, StyleFormer. It first proposes the concept of spatial–temporal style base and no longer constrains to semantic representation in a single image style. Instead, it introduces a spatial–temporal interactive style projection layer between bitemporal images, which projects the unseen diverse styles into the consistent expression space for change detection. Furthermore, an iterative interaction strategy of Transformer and CNN features is proposed to mine spatial–temporal context information more finely. It solves the lack of local perception and nonhierarchical features in ViT, and improves the model expression ability. After that, a change prior-guided cross-attention is introduced to fuse bitemporal features. It can adaptively enhance the change feature and improve the perception ability for small changes in remote sensing scenes. Sufficient experiments on four typical change detection datasets show that the proposed method is superior to the state-of-the-art methods. Especially on the datasets CDD-CD and SYSU-CD, the F1 score improved to 96.08% and 83.29%. The code of this work will be available athttps://github.com/Tom-Dongfang/change-detection-StyleFormer.

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.

In the field of remote sensing, detection in dimly lit or shadowed areas has traditionally been difficult because of detector noise. Given that noise in real-world images of remote sensing exhibits spatial correlation, existing self-supervised methods encounter difficulties in reconciling the suppression of spatially correlated noise with the preservation of local texture details. To address this challenge, we propose a self-supervised model that combines blind-spot feature extraction with diffusion-based texture generation to fine denoising of real-world images under adverse conditions. We first introduce a blind-spot feature extraction structure based on the fusion of U-Net with blind-spot net (UBSN) and blind transformer (BTF). In UBSN, we integrate multistride blind-spot convolution (BSC) + dilated convolution (DC) feature extraction nodes and employ a Reshuffle strategy in skip-layer connections to maintain large-scale blind-spot characteristics. Additionally, we design a transformer structure for blind spot between patches to remove the noise with spatial correlations while ensuring global feature acquisition. Subsequently, to restore texture details blurred by the blind-spot structures, we introduce a texture generation diffusion structure during model training, achieving a balance between large-scale blind-spot characteristics and local rich texture details. Experimental results demonstrate that our approach outperforms other self-supervised denoising methods, even some methods leveraging unpaired images, without the need for parameters related to on-orbit satellite detectors.

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.

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.

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.