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.

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.

Remote sensing image change detection is an important means for earth monitoring task, which has a wide application prospect. In multi-temporal 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 multi-temporal 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 bi-temporal 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 non-hierarchical features in ViT, and improves the model expression ability. After that, a change prior-guided cross-attention is introduced to fuse bi-temporal 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 dataset CDD-CD and SYSU-CD, the F1 score improved to 96.08% and 83.29%. The code of this work will be available at https://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% AP@0.5, and 1.6% AP@0.5:0.95 in the proposed dataset.

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.