A5100448367- Advanced Neural Network Applications
- Remote-Sensing Image Classification
- Advanced Image and Video Retrieval Techniques
- Medical Image Segmentation Techniques
- Advanced Image Fusion Techniques
- Robotics and Sensor-Based Localization
- Image and Signal Denoising Methods
- Automated Road and Building Extraction
- Advanced Image Processing Techniques
- Remote Sensing and LiDAR Applications
- Image Retrieval and Classification Techniques
- Image Processing Techniques and Applications
- Conducting polymers and applications
- Optical Systems and Laser Technology
- Remote Sensing and Land Use
- Domain Adaptation and Few-Shot Learning
- Traditional Chinese Medicine Studies
- Organic Electronics and Photovoltaics
- Energy Load and Power Forecasting
- Handwritten Text Recognition Techniques
- Image Enhancement Techniques
- 3D Shape Modeling and Analysis
- Medical Imaging and Analysis
- Image Processing and 3D Reconstruction
- Text and Document Classification Technologies
Tsinghua University
2004-2024
Wuhan University
2008-2024
Xi'an University of Technology
2024
University of Manitoba
2018-2024
University of Warwick
2022-2024
Chinese Academy of Sciences
2020-2024
Southwest Petroleum University
2015-2024
Lanzhou University of Technology
2022-2024
Tsinghua Sichuan Energy Internet Research Institute
2024
Beijing Institute of Nanoenergy and Nanosystems
2024
Semantic segmentation of remote sensing images plays an important role in a wide range applications including land resource management, biosphere monitoring and urban planning. Although the accuracy semantic has been increased significantly by deep convolutional neural networks, several limitations exist standard models. First, for encoder-decoder architectures such as U-Net, utilization multi-scale features causes underuse information, where low-level high-level are concatenated directly...
In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI in this paper. Two branches are designed DBDA to capture plenty of spectral spatial features contained HSI. Furthermore, channel block applied these two respectively, which enables refine optimize extracted feature maps. A series...
The fully convolutional network (FCN) with an encoder-decoder architecture has been the standard paradigm for semantic segmentation. utilizes encoder to capture multilevel feature maps, which are incorporated into final prediction by a decoder. As context is crucial precise segmentation, tremendous effort made extract such information in intelligent fashion, including employing dilated/atrous convolutions or inserting attention modules. However, these endeavors all based on FCN ResNet other...
Semantic segmentation of remotely sensed imagery plays a critical role in many real-world applications, such as environmental change monitoring, precision agriculture, protection, and economic assessment. Following rapid developments sensor technologies, vast numbers fine-resolution satellite airborne remote sensing images are now available, for which semantic is potentially valuable method. However, because the rich complexity heterogeneity information provided with an ever-increasing...
Oxidation flow reactors (OFRs) containing low-pressure mercury (Hg) lamps that emit UV light at both 185 and 254 nm ("OFR185") to generate OH radicals O3 are used in many areas of atmospheric science pollution control devices. The widely potential aerosol mass (PAM) OFR was designed for studies on the formation oxidation secondary organic aerosols (SOA), allowing a wide range oxidant exposures short experiment duration with reduced wall loss effects. Although fundamental photochemical...
The attention mechanism can refine the extracted feature maps and boost classification performance of deep network, which has become an essential technique in computer vision natural language processing. However, memory computational costs dot-product increase quadratically with spatiotemporal size input. Such growth hinders usage mechanisms considerably application scenarios large-scale inputs. In this letter, we propose a linear (LAM) to address issue, is approximately equivalent...
Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, planning, etc. However, the tremendous details contained VFR image, especially considerable variations scale and appearance of objects, severely limit potential existing deep learning approaches. Addressing such issues represents promising research field remote sensing community, which paves way for...
As an important carrier of human productive activities, the extraction buildings is not only essential for urban dynamic monitoring but also necessary suburban construction inspection. Nowadays, accurate building from remote sensing images remains a challenge due to complex background and diverse appearances buildings. The convolutional neural network (CNN) based methods, although increased accuracy significantly, are criticized their inability modelling global dependencies. Thus, this paper...
The thriving development of earth observation technology makes more and high-resolution remote-sensing images easy to obtain. However, caused by fine-resolution, the huge spatial spectral complexity leads automation semantic segmentation becoming a challenging task. Addressing such an issue represents exciting research field, which paves way for scene-level landscape pattern analysis decision-making. To tackle this problem, we propose approach automatic land based on Feature Pyramid Network...
In this paper, a Multi-Scale Fully Convolutional Network (MSFCN) with multi-scale convolutional kernel is proposed to exploit discriminative representations from two-dimensional (2D) satellite images.
Abstract. Field studies in polluted areas over the last decade have observed large formation of secondary organic aerosol (SOA) that is often poorly captured by models. The study SOA using ambient data confounded effects advection, vertical mixing, emissions, and variable degrees photochemical aging. An oxidation flow reactor (OFR) was deployed to real-time during California Research at Nexus Air Quality Climate Change (CalNex) campaign Pasadena, CA, 2010. A high-resolution mass spectrometer...
Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder–decoder architecture, has been used frequently for image with high accuracy. In this letter, we incorporate multiscale features generated by different layers U-Net design skip connected asymmetric-convolution-based (MACU-Net), using fine-resolution images. Our the following advantages: (1) connections combine realign...
In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI in this paper. Two branches are designed DBDA to capture plenty of spectral spatial features contained HSI. Furthermore, channel block applied these two respectively, which enables refine optimize extracted feature maps. A series...
Semantic segmentation of remote sensing images plays a crucial role in wide variety practical applications, including land cover mapping, environmental protection, and economic assessment. In the last decade, convolutional neural network (CNN) is mainstream deep learning-based method semantic segmentation. Compared with conventional methods, CNN-based methods learn features automatically, thereby achieving strong representation capability. However, local receptive field convolution operation...
Wake interactions between wind turbines have a great impact on the overall performance of farm. In this work, novel deep learning method, called Bilateral Convolutional Neural Network (BiCNN), is proposed and then employed to accurately model dynamic farm wakes based flow field data generated by high-fidelity simulations. Different from existing machine-learning-based wake models where dimensionality reduction essential, BiCNN designed directly process different types inputs through...
Abstract The purpose of human object detection is to obtain the number people and their position in images, which one core problems field machine vision. However, high missing rate from small- medium-sized bodies due large variety scale tasks still influences performance detection. To solve above problem, this paper proposed an improved ASPP_BiFPN_YOLOv4 (ABYOLOv4) method detect In detail, Atrous Spatial Pyramid Pooling (ASPP) module was used replace original increase receptive level network...
A general and practical strategy has been developed to prepare aurone derivatives.
The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion the key realizing high-precision underwater positioning. To solve problem, a scheme based on factor graph optimization (FGO) proposed. Due iterations joint of historical data, FGO could show better performance than traditional Kalman filter....
Multi-modality medical imaging is crucial in clinical treatment as it can provide complementary information for image segmentation. However, collecting multi-modal data difficult due to the limitation of scan time and other situations. As such, clinically meaningful develop an segmentation paradigm handle this missing modality problem. In paper, we propose a prototype knowledge distillation (ProtoKD) method tackle challenging problem, especially toughest scenario when only single modal be...
AlN/diamond heterostructures hold tremendous promise for the development of next-generation high-power electronic devices due to their ultrawide band gaps and other exceptional properties. However, poor adhesion at interface is a significant challenge that will lead film delamination device performance degradation. In this study, uniaxial tensile failure heterogeneous interfaces was investigated by molecular dynamics simulations based on neuroevolutionary machine learning potential (NEP)...
A novel class of light harvesting conjugated block copolymers, with electron-donating blocks (D) connected to electron-accepting (A) via non and flexible bridge chains (B), has been designed, synthesized, characterized. Specifically, D is a decyloxy-substituted polyphenylenevinylene (C10−PPV). A1 A2 are PPVs sulfone (SO2) acceptor moieties substituted on every other phenylene unit. carries two decyloxy groups unit, while in A2, half the units unsubstituted. The optical energy gaps 2.24 eV...