Land surface temperature (LST) is essential for studying land–atmosphere energy exchange, the impact of climate change, and its influence on crop yields and hydrology. Although satellite remote sensing provides large-scale LST data, existing sp...
This paper proposes a spatially adaptive feature fine fusion network consisting of a Fast Convolution Decomposition Sequence (FCDS) and a Spatial Selection Mechanism (SSM). Firstly, in FCDS, a large kernel convolution decomposition operation is used...
Detecting small objects in complex outdoor conditions remains challenging. This paper proposes an improved version of YOLOv8n for the detection of blueberry in challenging outdoor scenarios. In this context, this article addresses feature extraction,...
Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. Many algorithms focus on the deep extraction of a single kind of feature to improve classification. There have been few studies on the deep extrac...
Forest fires occur frequently around the world, causing serious economic losses and human casualties. Deep learning techniques based on convolutional neural networks (CNN) are widely used in the intelligent detection of forest fires. However, CNN-bas...
The safety of autonomous driving is closely linked to accurate depth perception. With the continuous development of autonomous driving, depth completion has become one of the crucial methods in this field. However, current depth completion methods ha...
Scene text detection task aims to precisely localize text in natural environments. At present, the application scenarios of text detection topics have gradually shifted from plain document text to more complex natural scenarios. Objects with similar...
Spectral reconstruction (SR) from multispectral images (MSIs) is a crucial task in remote sensing image processing, aiming to enhance the spectral resolution of MSIs to produce hyperspectral images (HSIs). However, most existing deep learning-based S...
In response to the challenges of remote sensing imagery, such as unmanned aerial vehicle (UAV) aerial imagery, including differences in target dimensions, the dominance of small targets, and dense clutter and occlusion in complex environments, this p...
Stored wheat pests threaten food quality and economic returns, yet existing detection methods struggle with small-object detection, complex scenarios, and efficiency–accuracy trade-offs, largely due to the lack of high-quality datasets. To addr...
Due to the unique imaging mechanism of synthetic aperture radar (SAR), targets in SAR images often shows complex scattering characteristics, including unclear contours, incomplete scattering spots, attitude sensitivity, etc. Automatic aircraft detect...
Problems such as complex image backgrounds, low image quality, diverse text forms, and similar or common character layouts in different script categories in natural scenes pose great challenges to scene script identification. This paper proposes a ne...
The quality of silkworm cocoons affects the quality and cost of silk processing. It is necessary to sort silkworm cocoons prior to silk production. Cocoon images consist of fine-grained images with large intra-class differences and small inter-class...
Multi-rotor drones have a wide range of applications in practical scenarios; however, the use of multi-rotor drones for illegal acts is also on the rise, in order to improve the recognition accuracy of multi-rotor drones. A new multi-rotor drone dete...
Due to the challenges posed by high resolution, substantial background noise, significant object scale variation, and long-tailed data distribution in remote sensing images, traditional techniques often struggle to maintain both high accuracy and low...
Road traffic elements are important components of roads and the main elements of structuring basic traffic geographic information databases. However, the following problems still exist in the detection and recognition of road traffic elements: dense...
Rice plays a crucial role in global agricultural production, but various foreign objects often mix in during its processing. To efficiently and accurately detect small foreign objects in the rice processing pipeline, ensuring food quality and consume...
In dense scenes, pedestrians often exhibit a variety of symmetrical features, such as symmetry in body contour, posture, clothing, and appearance. However, pedestrian detection poses challenges due to the mutual occlusion of pedestrians and the small...
Hyperspectral sparse unmixing (SU) networks have recently received considerable attention due to their model hyperspectral images (HSIs) with a priori spectral libraries and to capture nonlinear features through deep networks. This method effectively...
Point cloud semantic segmentation is a key technology for applications such as autonomous driving, robotics, and virtual reality. Current approaches are heavily reliant on local relative coordinates and simplistic attention mechanisms to aggregate ne...
At present, deep learning has been widely used in SAR ship target detection, but the accurate and real-time detection of multi-scale targets still faces tough challenges. CNN-based SAR ship detectors are challenged to meet real-time requirements beca...
The cultivation of the Chinese mitten crab (Eriocheir sinensis) is an important component of China’s aquaculture industry and also a field of concern worldwide. It focuses on the selection of high-quality, disease-free juvenile crabs. However,...
(1) Background: With the continuous development of intelligent education, classroom behavior recognition has become increasingly important in teaching evaluation and learning analytics. In response to challenges such as occlusion, scale differences,...
The belt conveyor is the main equipment for underground coal transportation. Its coal flow is mixed with large coal, gangue, anchor rods, wooden strips, and other foreign objects, which easily causes failure of the conveyor belt, such as scratching,...
Airport detection in remote sensing scenes is a crucial area of research, playing a key role in aircraft blind landing procedures. However, airport detection in remote sensing scenes still faces challenges such as class confusion, poor detection perf...
Existing 3D point cloud enhancement methods typically rely on artificially designed geometric transformations or local blending strategies, which are prone to introducing illogical deformations, struggle to preserve global structure, and exhibit insu...
Multi-platform aerial remote sensing supports critical applications including wide-area surveillance, traffic monitoring, maritime security, and search and rescue. However, constrained by observation altitude and sensor resolution, targets inherently...
Hyperspectral image (HSI) classification faces challenges such as high dimensionality, spectral redundancy, and difficulty in modeling the coupling between spectral and spatial features. Existing methods fail to fully exploit first-order derivatives...
China has a large number of transmission lines laid in the mountains and forests and other regions, and these transmission lines enable national strategic projects such as the west-east power transmission project. However, the occurrence of mountain...
Facial Expression Recognition (FER) is a research topic of great practical significance. However, existing FER methods still face numerous challenges, particularly in the interaction between spatial and global information, the distinction of subtle e...
Current mainstream remote sensing target detection algorithms mostly estimate the rotation angle of targets by designing different bounding box descriptions and loss functions. However, they fail to consider the symmetry–asymmetry duality aniso...
Unmixing based fusion aims at generating a high spectral-spatial resolution image (HSS) with the same surface features of the high spatial resolution multispectral image (MS) and low spatial resolution hyperspectral image (HS). In this paper, a new f...
Video salient object detection has attracted growing interest in recent years. However, some existing video saliency models often suffer from the inappropriate utilization of spatial and temporal cues and the insufficient aggregation of different lev...
Object detection in remote sensing images faces the challenges of a complex background, large object size variations, and high inter-class similarity. To address these problems, we propose an adaptive adjacent layer feature fusion (AALFF) method, whi...
Synthetic aperture radar (SAR) image target recognition has important application values in security reconnaissance and disaster monitoring. However, due to speckle noise and target orientation sensitivity in SAR images, traditional spatial domain re...
Remote sensing image fusion can effectively solve the inherent contradiction between spatial resolution and spectral resolution of imaging systems. At present, the fusion methods of remote sensing images based on multi-scale transform usually set fus...
The joint classification of hyperspectral imagery (HSI) and LiDAR data is an important task in the field of remote sensing image interpretation. Traditional classification methods, such as support vector machine (SVM) and random forest (RF), have dif...
Wrinkles, crucial for age estimation and skin quality assessment, present challenges due to their uneven distribution, varying scale, and sensitivity to factors like lighting. To overcome these challenges, this study presents facial wrinkle detection...
To tackle the challenges of relative attitude adaptability and limited sample availability in underwater moving target recognition for active sonar, this study focuses on key aspects such as feature extraction, network model design, and information f...
Graph convolutional networks are widely used in skeleton-based action recognition because of their good fitting ability to non-Euclidean data. While conventional multi-scale temporal convolution uses several fixed-size convolution kernels or dilation...
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...
In contrast to conventional remote sensing images, hyperspectral remote sensing images are characterized by a greater number of spectral bands and exceptionally high resolution. The richness of both spectral and spatial information facilitates the pr...
In hyperspectral image classification (HSIC), every pixel of the HSI is assigned to a land cover category. While convolutional neural network (CNN)-based methods for HSIC have significantly enhanced performance, they encounter challenges in learning...
Water body extraction plays a crucial role in remote sensing, supporting applications such as environmental monitoring and disaster prevention. Although Deep Convolutional Neural Networks (DCNNs) have achieved remarkable progress, their hierarchical...
In response to the issue that the fusion process of infrared and visible images is easily affected by lighting factors, in this paper, we propose an adaptive illumination perception fusion mechanism, which was integrated into an infrared and visible...
To address the limitations of traditional two-stream networks, such as inadequate spatiotemporal information fusion, limited feature diversity, and insufficient accuracy, we propose an improved two-stream network for human action recognition based on...
Existing deep learning-based spatiotemporal fusion (STF) methods for remote sensing imagery often focus exclusively on capturing temporal changes or enhancing spatial details while failing to fully leverage spectral information from coarse images. To...
As one type of object detection, small object detection has been widely used in daily-life-related applications with many real-time requirements, such as autopilot and navigation. Although deep-learning-based object detection methods have achieved gr...
Recent advances in hyperspectral image (HSI) classification have demonstrated the effectiveness of hybrid architectures that integrate convolutional neural networks (CNNs) and Transformers, leveraging CNNs for local feature extraction and Transformer...
In agricultural pest detection, the small size of pests poses a critical hurdle to detection accuracy. To mitigate this concern, we propose a Lightweight Cross-Level Feature Aggregation Network (LCFANet), which comprises three key components: a deep...
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