Wenzhi Liao
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View article: Centroid growth selective clustering method for surface defect detection in silicon nitride ceramic bearing rollers
Centroid growth selective clustering method for surface defect detection in silicon nitride ceramic bearing rollers Open
Surface defects on silicon nitride ceramic bearing rollers typically exhibit fuzzy edge characteristics and gradient plunge features, which present significant challenges in image segmentation, including contour anomalies, incomplete segme…
View article: Examining changes in the growth competition index of trees caused by typhoon damage using multitemporal airborne lidar scanning data
Examining changes in the growth competition index of trees caused by typhoon damage using multitemporal airborne lidar scanning data Open
Typhoon damage refers to the extensive destruction of natural and environmental resources caused by powerful tropical storms, which are characterized by high winds and heavy rainfall. The impacts of these natural disasters on mountainous l…
View article: NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction
NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction Open
Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber…
View article: NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction
NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction Open
Track: New technologies for neutrino physics
View article: Lightweight Oriented Detector for Insulators in Drone Aerial Images
Lightweight Oriented Detector for Insulators in Drone Aerial Images Open
Due to long-term exposure to the wild, insulators are prone to various defects that affect the safe operation of the power system. In recent years, the combination of drones and deep learning has provided a more intelligent solution for in…
View article: Active learning for gear defect detection in gearboxes
Active learning for gear defect detection in gearboxes Open
Condition monitoring of gears in gearboxes is crucial to ensure performance and minimizing downtime in many industrial applications including wind turbines and automotive. Monitoring techniques using indirect measurements (i.e. acceleromet…
View article: Scale value guided Lite-FCOS for pointer meter reading recognition
Scale value guided Lite-FCOS for pointer meter reading recognition Open
As intelligent power grid construction advances, substation inspection becomes crucial, particularly in identifying meter readings. Existing meter reading methods are mainly based on the relationship between pointer and scale. However, the…
View article: Frequency‐to‐spectrum mapping GAN for semisupervised hyperspectral anomaly detection
Frequency‐to‐spectrum mapping GAN for semisupervised hyperspectral anomaly detection Open
Most unsupervised or semisupervised hyperspectral anomaly detection (HAD) methods train background reconstruction models in the original spectral domain. However, due to the noise and spatial resolution limitations, there may be a lack of …
View article: Panchromatic and Hyperspectral Image Fusion: Outcome of the 2022 WHISPERS Hyperspectral Pansharpening Challenge
Panchromatic and Hyperspectral Image Fusion: Outcome of the 2022 WHISPERS Hyperspectral Pansharpening Challenge Open
This paper presents the scientific outcomes of the 2022 Hyperspectral Pansharpening Challenge organized by the 12th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE WHISPERS 2022). The 2022 Hype…
View article: Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming
Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming Open
Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a …
View article: Cross-Domain Classification of Multisource Remote Sensing Data Using Fractional Fusion and Spatial-Spectral Domain Adaptation
Cross-Domain Classification of Multisource Remote Sensing Data Using Fractional Fusion and Spatial-Spectral Domain Adaptation Open
Limitation of labeled samples has always been a challenge for hyperspectral image (HSI) classification. In real remote sensing applications, we encounter a situation where an HSI scene is not labeled at all. To solve this problem, cross-do…
View article: The New Working Groups of the GRSS Technical Committee on Image Analysis and Data Fusion [Technical Committees]
The New Working Groups of the GRSS Technical Committee on Image Analysis and Data Fusion [Technical Committees] Open
Presents information on the GRSS echnical Committee on Image Analysis and Data Fusion.
View article: Multilayer Sparsity-Based Tensor Decomposition for Low-Rank Tensor Completion
Multilayer Sparsity-Based Tensor Decomposition for Low-Rank Tensor Completion Open
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank (LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes hidden in a tensor, we propose a new multilayer sp…
View article: Fractional Gabor Convolutional Network for Multisource Remote Sensing Data Classification
Fractional Gabor Convolutional Network for Multisource Remote Sensing Data Classification Open
Remote sensing using multisensor platforms has been systematically applied for monitoring and optimizing human activities. Several advanced techniques have been developed to enhance and extract the spatially and spectrally semantic informa…
View article: Coupled Convolutional Neural Network With Adaptive Response Function Learning for Unsupervised Hyperspectral Super Resolution
Coupled Convolutional Neural Network With Adaptive Response Function Learning for Unsupervised Hyperspectral Super Resolution Open
International audience
View article: Global Spatial and Local Spectral Similarity-Based Manifold Learning Group Sparse Representation for Hyperspectral Imagery Classification
Global Spatial and Local Spectral Similarity-Based Manifold Learning Group Sparse Representation for Hyperspectral Imagery Classification Open
International audience
View article: A Deep-Neural-Network-Based Hybrid Method for Semi-Supervised Classification of Polarimetric SAR Data
A Deep-Neural-Network-Based Hybrid Method for Semi-Supervised Classification of Polarimetric SAR Data Open
This paper proposes a deep-neural-network-based semi-supervised method for polarimetric synthetic aperture radar (PolSAR) data classification. The proposed method focuses on achieving a well-trained deep neural network (DNN) when the amoun…
View article: Semi-Supervised Classification of Polarimetric SAR Images Using Markov Random Field and Two-Level Wishart Mixture Model
Semi-Supervised Classification of Polarimetric SAR Images Using Markov Random Field and Two-Level Wishart Mixture Model Open
In this work, we propose a semi-supervised method for classification of polarimetric synthetic aperture radar (PolSAR) images. In the proposed method, a 2-level mixture model is constructed by associating each component density with a uniq…
View article: Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields
Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields Open
Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels. Conventional classification techniques do n…
View article: Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction
Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction Open
Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR method…
View article: Hyperspectral and multispectral image fusion via tensor sparsity regularization
Hyperspectral and multispectral image fusion via tensor sparsity regularization Open
Hyperspectral image (HSI) super-resolution scheme based on HSI and multispectral image (MSI) fusion has been a prevalent research theme in remote sensing. However, most of the existing HSI-MSI fusion (HMF) methods adopt the sparsity prior …
View article: Large-scale Landsat image classification based on deep learning methods
Large-scale Landsat image classification based on deep learning methods Open
Deep learning has demonstrated its superiority in computer vision. Landsat images have specific characteristics compared with natural images. The spectral and texture features of the same class vary along with the imaging conditions. In th…
View article: Deep Learning Fusion of RGB and Depth Images for Pedestrian Detection.
Deep Learning Fusion of RGB and Depth Images for Pedestrian Detection. Open
In this paper, we propose an effective method based on the Faster-RCNN structureto combine RGB and depth images for pedestrian detection. During the training stage,we generate a semantic segmentation map from the depth image and use it to …
View article: High-Resolution Aerial Imagery Semantic Labeling with Dense Pyramid Network
High-Resolution Aerial Imagery Semantic Labeling with Dense Pyramid Network Open
Semantic segmentation of high-resolution aerial images is of great importance in certain fields, but the increasing spatial resolution brings large intra-class variance and small inter-class differences that can lead to classification ambi…
View article: An Occlusion-Robust Feature Selection Framework in Pedestrian Detection †
An Occlusion-Robust Feature Selection Framework in Pedestrian Detection † Open
Better features have been driving the progress of pedestrian detection over the past years. However, as features become richer and higher dimensional, noise and redundancy in the feature sets become bigger problems. These problems slow dow…
View article: Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification
Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification Open
Spectral-spatial classification has been widely applied for remote sensing applications, especially for hyperspectral imagery. Traditional methods mainly focus on local spatial similarity and neglect nonlocal spatial similarity. Recently, …
View article: Total Variation and Rank-1 Constraint RPCA for Background Subtraction
Total Variation and Rank-1 Constraint RPCA for Background Subtraction Open
Background subtraction (BS) in video sequences is a main research field, and the aim is to separate moving objects in the foreground from stationary background. Using the framework of schemes-based robust principal component analysis (RPCA…