Graph Neural Networks (GNNs) have garnered widespread recognition in the identification of Autism Spectrum Disorder (ASD) owing to their remarkable adaptability to irregular patterns of Functional Brain Networks (FBNs). However, current methods for constructing FBNs generally employ a uniform modeling strategy to process neuroimaging data from different subjects, which fail to consider the heterogeneity of functional connectivity patterns among individuals adequately. In addition, existing methods tend to excessively focus on directly connected brain Regions of Interest (ROIs) when analyzing brain networks, underestimat\ing the importance of indirectly connected brain ROIs. At the same time, conventional approaches for identifying crucial brain regions may miss vital regions due to rigid threshold constraints. To address these issues, we propose Personalized Structure Preservation based GNN (PSP-GNN) for ASD diagnosis, which incorporates three aspects: 1) A personalized structure preservation strategy that constructs individualized brain networks by accounting for subject-specific variations; 2) A connection interaction-aware module designed to characterize interactions between directly and indirectly connected brain regions, providing comprehensive brain network representations; 3) A flexible brain region refinement technique based on Bernoulli sampling, which identifies salient brain regions without relying on pre-defined thresholds. Experimental results demonstrate the effectiveness of PSP-GNN in ASD diagnosis, highlighting its potential as a robust tool for future ASD diagnosis applications that combine FBNs and GNNs. Notably, the critical brain regions identified by PSP-GNN are consistent with established medical knowledge, suggesting their utility as potential biomarkers for clinical ASD diagnosis.

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.

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.

The high cost of maintaining radio map is a major hurdle for wide application of WLAN fingerprint-based indoor localization. The development of mobile crowd sensing provides new possibilities, however the features of normal users such as moving freely and non -professional bring new challenges. In this paper, a radio map self-updating algorithm is proposed to resolve three key problems: the localization accuracy, determination of fingerprints need to be updated, and capture of new fingerprints. First we design the localization matrix mechanism and periodic adaptive estimate algorithm to ensure the localization accuracy. Second we propose the fingerprint integrity assessment algorithm to detect the access points changed and the periodic adaptive estimate algorithm to decide the update period for each reference point. Finally we design the active fingerprint collecting mode to update the radio map efficiently. The algorithm proposed has been deployed for real-world testing over 30 days, our studies show that it detects the network changes in indoor environment correctly in 98% cases, and automatically judges the localization accuracy in 95% cases. Meanwhile, the localization accuracy is stable and improved by over 40% even after long terms of deployment, and the overhead of user terminals is reduced over 40%.