Hyperspectral imaging (HSI) is a powerful tool for precision imaging tasks such as vegetation analysis, but its widespread use remains limited due to the high cost of equipment and challenges in data acquisition. To explore a more accessible alternat...
Continual mapping and monitoring of impervious surfaces are crucial activities to support sustainable urban management strategies and to plan effective actions for environmental changes. In this context, impervious surface coverage is increasingly be...
Near-infrared spectroscopy (NIRS) is widely used in food, pharmaceutical, and agricultural analyses but is highly susceptible to noise. To address this, we propose BiLSTM-FuseNet, a denoising framework that combines temporal modeling and explicit noi...
This study investigates automated fake news detection as a reliability-oriented text classification problem in dynamic digital information environments. We propose H-FuseNet, a hybrid multi-representation fusion framework that combines pretrained tra...
Land cover mapping provides spatial information on the physical properties of the Earth’s surface for various classes of wetlands, artificial surface and constructions, vineyards, water bodies, etc. Having reliable information on land cover is...
To address the challenges of insufficient scale normalization, limited time–frequency localization, and ineffective multi-scale feature extraction in the intelligent fault diagnosis of rotating components under varying operating conditions, we...
Population aging and the rising prevalence of motor disorders are driving demand for assistive lower-limb robotic systems capable of decoding user intention rather than merely providing mechanical support. We present CM-FuseNet, an attention-augmente...
Sentiment analysis (SA) is an important task in natural language processing in which convolutional neural networks (CNNs) have been successfully applied. However, most existing CNNs can only extract predefined, fixed-scale sentiment features and cann...
In current road segmentation tasks, high-frequency details of roads (such as road edges and pavement textures) tend to become blurred or even lost during feature extraction due to progressive downsampling, leading to imprecise segmentation boundaries...
Accurate mapping of paddy rice is essential for agricultural monitoring, yield estimation, and food security assessment. However, optical imagery is often affected by clouds and spectral confusion, while SAR imagery suffers from speckle noise and wea...
Fine-grained land classification provides low-cost and efficient data support for land resource monitoring, agricultural assessment, and ecological protection. However, classification methods based on field-captured images often suffer from performan...
The selection and representation of classification features in remote sensing image play crucial roles in image classification accuracy. To effectively improve the features classification accuracy, an improved U-Net remote sensing classification algo...
Timely and accurate acquisition of crop type information is significant for irrigation scheduling, yield estimation, harvesting arrangement, etc. The unmanned aerial vehicle (UAV) has emerged as an effective way to obtain high resolution remote sensi...
Accurate diagnosis of pear tree nutrient deficiency symptoms is vital for the timely adoption of fertilization and treatment. This study proposes a novel method on the fused feature multi-head attention recording network with image depth and shallow...
Background: In breast cancer diagnosis, while radiological imaging modalities provide important insights into the structural characteristics of tumors, pathological examinations remain essential for establishing a definitive diagnosis. Methods: This...
It is of great significance to accurately identify soybean planting areas for ensuring agricultural and industrial production. High-resolution satellite remotely sensed imagery has greatly facilitated the effective extraction of soybean planting area...
Semantic segmentation of high-resolution remote sensing images holds paramount importance in the field of remote sensing. To better excavate and fully fuse the features in high-resolution remote sensing images, this paper introduces a novel Global an...
LiDAR-unmanned aerial system (LiDAR-UAS) technology can accurately and efficiently obtain detailed and accurate three-dimensional spatial information of objects. The classification of objects in estuarine areas is highly important for management, pla...
Assessment of photovoltaic panel surfaces must cope with illumination variation, dust, snow, glare, cracks and bird droppings. This study presents SolarVisionNet (SVN), which fuses a seven-channel input (RGB plus physics-inspired glare, snow, bird-dr...
Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFu...
Cross-border regions often exhibit complex vegetation-related land-cover patterns due to contrasting natural conditions and divergent development trajectories, causing multi-source land-cover products to suffer from disagreements in class assignment...
Approximately 1 billion people worldwide currently inhabit slum areas. The UN Sustainable Development Goal (SDG 11.1) underscores the imperative of upgrading all slums by 2030 to ensure adequate housing for everyone. Geo-locations of slums help local...
Cracks are common defects that occur on the surfaces of objects and structures. Crack detection is a critical maintenance task that traditionally requires manual labor. Large-scale manual inspections are expensive. Research has been conducted to repl...
Rapid and accurate landslide mapping is essential for disaster risk reduction and sustainable land management in landslide-prone mountainous regions. This study presents a U-Net semantic segmentation framework for pixel-wise landslide classification...
The identification of stay cable icing is crucial for robot deicing to improve efficiency and prevent damage to stay cables. Therefore, it is significant to identify the areas and degree of icing in the images of stay cables. This study proposed a tw...
Supervised contrastive learning has emerged as a novel method to help solve the problem of multivariate time-series classification. By utilizing labeled data, it maximally learns the feature-representation differences between various categories. Howe...
Ransomware detection remains challenging because modern variants exhibit diverse, elusive, and partly benign behaviors and can propagate rapidly across interconnected enterprises and sensor-enabled cyber-physical systems, causing cascading operationa...
Additive manufacturing (AM) monitoring is fundamentally constrained by the severe scarcity of annotated data for layer-wise segmentation. This paper addresses this bottleneck by introducing a scalable, high-fidelity synthetic data generation pipeline...
Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the ear...
As artificial intelligence develops, deep learning algorithms are increasingly being used in the field of dorsal hand vein (DHV) recognition. However, deep learning has high requirements regarding the number of samples, and current DHV datasets have...
Camera traps play an important role in biodiversity monitoring. An increasing number of studies have been conducted to automatically recognize wildlife in camera trap images through deep learning. However, wildlife recognition by camera trap images a...
Few-shot image classification seeks to recognize novel categories from only a handful of labeled examples, but conventional metric-based methods that rely mainly on global image features often produce unstable prototypes under extreme data scarcity,...
Image recognition has long been one of the research hotspots in computer vision tasks. The development of deep learning is rapid in recent years, and convolutional neural networks usually need to be designed with fixed resources. If sufficient resour...
Remote sensing (RS) image change detection (CD) is the procedure of detecting the change regions that occur in the same area in different time periods. A lot of research has extracted deep features and fused multi-scale features by convolutional neur...
With the popularity of mobile devices, lightweight deep learning models have important value in various application scenarios. However, how to effectively fuse the feature information from different dimensions while ensuring the model’s lightne...
In this paper, a novel Pixel-Voxel network is proposed for dense 3D semantic mapping, which can perform dense 3D mapping while simultaneously recognizing and labelling the semantic category each point in the 3D map. In our approach, we fully leverage...
Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical...
Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatolo...
Urban land use information is critical to urban planning, but the increasing complexity of urban systems makes the accurate classification of land use extremely challenging. Human activity features extracted from big data have been used for land use...
Hyperspectral images (HSI) frequently have inadequate spatial resolution, which hinders numerous applications for the images. High resolution multispectral image (MSI) has been fused with HSI to reconstruct images with both high spatial and high spec...
In indoor environments, reverberation can distort the signalseceived by active noise cancelation devices, posing a challenge to sound classification. Therefore, we combined three speech spectral features based on different frequency scales into a den...
Cervical cancer, a common chronic disease, is one of the most prevalent and curable cancers among women. Pap smear images are a popular technique for screening cervical cancer. This study proposes a computer-aided diagnosis for cervical cancer utiliz...
Carrier signal detection is a complicated and essential task in many domains because it demands a quick response to the existence of several carriers in the wideband, while also precisely predicting each carrier signal’s frequency centers and b...
This research proposes a deep learning method for classifying student behavior in classrooms that follow the professional learning community teaching approach. We collected data on five student activities: hand-raising, interacting, sitting, turning...
Change detection in high resolution (HR) remote sensing images faces more challenges than in low resolution images because of the variations of land features, which prompts this research on faster and more accurate change detection methods. We propos...
Foreign objects such as kites, nests and balloons, etc., suspended on transmission lines may shorten the insulation distance and cause short-circuits between phases. A detection method for foreign objects on transmission lines is proposed, which comb...
The use of neural networks for plant disease identification is a hot topic of current research. However, unlike the classification of ordinary objects, the features of plant diseases frequently vary, resulting in substantial intra-class variation; in...
Malignant lymphoma is one of the most severe types of disease that leads to death as a result of exposure of lymphocytes to malignant tumors. The transformation of cells from indolent B-cell lymphoma to B-cell lymphoma (DBCL) is life-threatening. Bio...
Proper identification of different grape varieties by smart machinery is of great importance to modern agriculture production. In this paper, a fast and accurate identification method based on Canonical Correlation Analysis (CCA), which can fuse diff...
A blur detection problem which aims to separate the blurred and clear regions of an image is widely used in many important computer vision tasks such object detection, semantic segmentation, and face recognition, attracting increasing attention from...
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