Pan-sharpening aims to generate high-resolution multispectral (HRMS) images by combining high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) data, while maintaining the symmetry of spatial and spectral characteristics....
In hyperspectral image classification (HSIC), each pixel contains information across hundreds of contiguous spectral bands; therefore, the ability to perform long-distance modeling that stably captures and propagates these long-distance dependencies...
Pansharpening seeks to reconstruct a high-resolution multi-spectral image (HR-MSI) by integrating the fine spatial details from the panchromatic (PAN) image with the spectral richness of the low-resolution multi-spectral image (LR-MSI). In recent yea...
Automatic modulation classification (AMC) under low signal-to-noise ratio (SNR) and complex channel conditions remains a significant challenge due to the trade-off between robustness and efficiency. This study proposes a lightweight temporal convolut...
Fabric defect detection plays a vital role in the quality control of the textile manufacturing industry. However, it remains challenging because of defect diversity, complexity, and environmental factors. Deep learning-based methods efficiently extra...
Image steganography is a research field that focuses on covert storage and transmission technologies. However, current image hiding methods based on invertible neural networks (INNs) have limitations in extracting image features. Additionally, after...
The Mamba network has recently emerged as a powerful architecture for remote sensing change detection (RSCD). Notably, the fusion of multi-scale features and heterogeneous attention mechanisms has proven effective in enhancing the performance of Mamb...
Hyperspectral target detection (HTD) aims to identify pixel-level targets within complex backgrounds, but existing HTD methods often fail to fully exploit multi-scale features and integrate global–local information, leading to suboptimal detect...
Multimodal image fusion aims to integrate complementary information from heterogeneous imaging modalities into a single informative image. However, many deep learning-based fusion methods rely on complex feature extractors, leading to high computatio...
The ubiquity of resource-constrained Internet-of Things (IoT) nodes creates an urgent demand for network intrusion detection systems (NIDSs) optimized for edge devices with limited computing power. In this paper, we propose a new NIDS system based on...
Accurate fault location in low-observability active distribution networks is hindered by uncertain inter-node relationships, underutilized early transients, and insufficient global context. To address these challenges, this paper proposes an adaptive...
Remote sensing image change captioning (RSICC) aims to describe changes between bi-temporal remote sensing (RS) images in natural language. The existing methods, typically based on traditional encoder–decoder architectures or multi-task collabo...
Hyperspectral images are rich in spectral and spatial information. However, their high dimensionality and complexity pose significant challenges for effective feature extraction. Specifically, the performance of existing models for hyperspectral imag...
Background: With the development of wireless communication technologies, the rapid advancement of 5G and 6G communication systems has spawned an urgent demand for low latency and high data rates. Orthogonal Frequency Division Multiplexing (OFDM) comm...
Selective state space models (SSMs) enable efficient long-range dependency modeling across language, vision, and 3D domains. However, because SSMs are optimized for sequential processing, they handle spatially irregular inputs such as point clouds po...
Multimodal remote-sensing classification aims to improve pixel-level land-cover recognition by exploiting complementary information from hyperspectral images and auxiliary modalities. Under limited labelled data and pronounced cross-modal discrepanci...
Hyperspectral image super-resolution (HSI-SR) has been widely applied in ground object classification, target detection, and environmental monitoring. Its core is to reconstruct high-quality spatial–spectral information from low-resolution obse...
With the increasing demand for higher precision and real-time performance in industrial surface defect detection, multimodal detection methods integrating RGB images and 3D point clouds have drawn considerable attention. However, current mainstream m...
The field of underwater image enhancement (UIE) has advanced significantly, yet it continues to grapple with persistent challenges stemming from complex, spatially varying optical degradations such as light absorption, scattering, and color distortio...
Temperature forecasting from distributed temperature sensing supports thermal characterization and ventilation decisions in grain storage. However, existing studies mainly focus on individual sensor points and are insufficient to describe spatial the...
Deep-learning-based methods have achieved remarkable success in hyperspectral image (HSI) classification tasks due to their promising ability. However, the high dimensionality and spectral–spatial correlations of HSIs usually lead to informatio...
This paper proposes a novel multimodal emotion recognition framework, termed Sparse Alignment and Liquid-Mamba (SALM), which effectively integrates the complementary strengths of Mamba networks and Liquid Neural Networks (LNNs). To capture neural dyn...
Deep learning has demonstrated significant success in hyperspectral image (HSI) classification by effectively leveraging spatial–spectral feature learning. However, current approaches encounter three challenges: (1) high spectral redundancy and...
The geolocalization of unmanned aerial vehicles (UAVs) in satellite-denied environments has emerged as a key research focus. Recent advancements in this area have been largely driven by learning-based frameworks that utilize convolutional neural netw...
Hyperspectral image classification is an important task in remote sensing image interpretation. However, existing Mamba-based methods remain limited in exploiting shallow frequency information, preserving neighborhood continuity during spatial serial...
Satellite remote sensing plays an essential role in wildfire monitoring due to its large-scale observation capability. However, fire targets in satellite imagery are typically extremely small, sparsely distributed, and embedded in complex backgrounds...
The semantic segmentation of satellite and UAV remote sensing imagery is pivotal for address exploration, change detection, quantitative analysis and urban planning. Recent advancements have seen an influx of segmentation networks utilizing convoluti...
In highly dynamic and interactive vehicular environments, accurate and efficient trajectory prediction is critical for driving safety and traffic efficiency. To overcome the limitations of existing methods, including degradation of long-range histori...
Reliable medical image segmentation remains challenging because models must preserve fine boundary details while maintaining global semantic consistency. CNNs capture local structures effectively but have limited long-range modeling ability, whereas...
Millimeter-wave (mmWave) radar provides a privacy-preserving and illumination-robust sensing modality for contactless gesture recognition. However, sparse radar point clouds degrade substantially as sensing distance increases: the number of valid det...
Hyperspectral image classification (HSIC) plays a crucial role in fine-grained Earth observation tasks. However, balancing efficient long-range dependency modeling with the extraction of fine-grained local features remains a significant challenge, pr...
Understanding how heating, ventilation, and air-conditioning (HVAC) operating-state predictability and predictive information change with forecast horizon is important for occupant-centric building operation. To systematically examine these changes f...
Hyperspectral unmixing aims to recover endmembers and their corresponding abundances from mixed pixels, which is a key task in quantitative hyperspectral remote sensing. Although convolutional neural networks (CNNs) are effective in modeling local sp...
Remote sensing image-to-online map translation (RSMT) represents a critical research frontier in geographic information processing. With the rapid development of deep learning, generative models have attracted increasing attention for automatic map g...
Skeleton-based action recognition networks have widely adopted the approach of Graph Convolutional Networks (GCN) due to their superior capabilities in modeling data topology, but several key issues still require further investigation. Firstly, the g...
Single image dehazing remains challenging because haze simultaneously distorts global illumination, scene structure, and fine textures, making rigid low–high frequency decoupling prone to error propagation and detail inconsistency. To address t...
Although state space models (SSMs) represented by Mamba have achieved long-range dependency modeling with linear complexity, they still struggle to realize differentiated processing of high-frequency and low-frequency components in image super-resolu...
Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields...
Background: Major depressive disorder (MDD) is a common psychiatric disorder. Electroencephalography (EEG) provides physiological information for depression detection, but many existing methods rely on dense multichannel recordings, limiting their us...
Spatial transcriptomics (ST) has emerged as an essential technology for interpreting the molecular profiles underlying pathological tissue morphology. Most existing ST analyses are limited to 2D sections, which ignore the complex structural and molec...
Background: Major depressive disorder (MDD) affects 332 million people worldwide, yet diagnosis remains reliant on subjective clinical interviews with substantial inter-rater variability. Objective neuroimaging model-attributed regions offer a path t...
Recently, a network based on selective state space models (SSMs), Mamba, has emerged as a research focus in hyperspectral image (HSI) classification due to its linear computational complexity and strong long-range dependency modeling capability. Orig...
The prompt and precise detection of road damage is vital for effective infrastructure management, forming the foundation for intelligent transportation systems and cost-effective pavement maintenance. While current convolutional neural network (CNN)-...
Vessel trajectory prediction (VTP) plays a critical role in maritime safety and intelligent navigation. Existing methods struggle to simultaneously capture long-term dependencies and nonlinear dynamic patterns in vessel movements. To address this cha...
The widespread availability of image editing tools and generative AI has made image forgery more accessible and deceptive, demanding more advanced localization techniques. Existing CNN-based methods are limited by local receptive fields, struggling w...
Remote sensing image change captioning (RSICC) aims to generate semantic textual descriptions characterizing changes between bi-temporal remote sensing images, with wide applications in disaster assessment and urban planning. However, existing method...
Automatically extracting landslide regions from remote sensing images plays a vital role in the landslide inventory compilation. However, this task remains challenging due to the considerable diversity of landslides in terms of morphology, triggering...
With the rapid development of aerospace technology, remote sensing images contain increasingly rich feature information, while large variations in object scales and imbalanced category distributions pose great challenges to remote sensing semantic se...
To tackle the challenges of unknown image distortion and catastrophic forgetting in incremental inverse synthetic aperture radar (ISAR) target classification, this article introduces a deformation-robust non-exemplar incremental ISAR target classific...
Soybean is an important food and oil crop, and rapid nondestructive identification of seed cultivars is crucial for seed purity inspection, varietal certification, breeding management and food-quality control. However, the global spectral profiles of...
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