A5075718042- Remote-Sensing Image Classification
- Remote Sensing and Land Use
- Advanced Image Fusion Techniques
- Sparse and Compressive Sensing Techniques
- Advanced Chemical Sensor Technologies
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Advanced Text Analysis Techniques
- Remote Sensing and LiDAR Applications
- Natural Language Processing Techniques
- Image and Signal Denoising Methods
- Topic Modeling
- Advanced Image and Video Retrieval Techniques
- Image Processing Techniques and Applications
- Financial Markets and Investment Strategies
- Remote Sensing in Agriculture
- Enterobacteriaceae and Cronobacter Research
- S100 Proteins and Annexins
- Infrared Target Detection Methodologies
- Insurance and Financial Risk Management
- Advanced MEMS and NEMS Technologies
- Advanced Sensor Technologies Research
- Bacterial Identification and Susceptibility Testing
- Advanced Sensor and Energy Harvesting Materials
- Genomics and Phylogenetic Studies
- Banking stability, regulation, efficiency
China University of Geosciences
2023-2024
Xidian University
2018-2024
Children's Hospital of Zhejiang University
2023
Beihang University
2023
Lanzhou Army General Hospital
2020
Beijing Normal University
2020
Kunming University of Science and Technology
2017
University of Bedfordshire
2013
Hyperspectral anomaly detection (HAD) is a challenging task since it identifies the targets without prior knowledge. In recent years, deep learning methods have emerged as one of most popular algorithms in HAD. These operate on assumption that background well reconstructed while anomalies cannot, and degree for each pixel represented by reconstruction errors. However, approaches treat all pixels hyperspectral image (HSI) type ground object. This does not always hold practical scenes, making...
Hyperspectral anomaly detection (HAD) is a challenging task in hyperspectral image processing, which to capture the by spectral and spatial information without prior knowledge. Recently, some isolation forest (IF) methods HAD are proposed achieve good accuracy. However, these build trees global pixels single band partition, way limits utilization of spectral-spatial information, resulting suffering from poor performance detecting hard anomalies. To this end, novel two-stream based on deep...
Sparse representation (SR)-based approaches and collaborative (CR)-based methods are proved to be effective detect the anomalies in a hyperspectral image (HSI). Nevertheless, existing for achieving anomaly detection (HAD) generally only consider one of them, failing comprehensively exploit them further promote performance. To address issue, novel HAD method, which integrates both SR CR, is proposed this article. specific, an model, whose overcomplete dictionary generated by means...
The low rank and sparse representation (LRSR) technique has attracted increasing attention for hyperspectral anomaly detection (HAD). Although a large quantity of research based on LRSR HAD is proposed, the performance still limited, due to unsatisfactory dictionary construction insufficient consideration global local characteristics. To tackle above-mentioned concern, novel method, termed dual collaborative constraints regularized low-rank via robust dictionaries construction, proposed in...
The imaging process of real-world images is inevitably polluted by noise, which affects the visual quality and subsequent processing images. How to restore image details while removing noise has always been a challenging problem. existing complementary learning strategies combine advantages both denoised have good effects. However, these methods that are based on single generative adversarial network (GAN) suffer from complex structure, difficulty in training, further improvement. Therefore,...
The quasi-static assumption of channels becomes invalid in a number emerging applications massive multiple-input multiple-output (MIMO) systems with high base station (BS)/user mobility, such as speed train and unmanned aerial vehicle communications. In these situations, the time variation shortens channel coherence decreases efficiency traditional estimation schemes significantly. This paper focuses on time-varying problem MIMO systems, where mobility BS/user is assumed to be high. sparse...
Hyperspectral anomaly detection (HAD), which is widely used in military and civilian fields, aims to detect the pixels with large spectral deviation from background. Recently, collaborative representation using union dictionary (CRUD) was proved be effective for achieving HAD. However, existing CRUD detectors generally only use spatial or information construct (UD), possibly causes a suboptimal performance may hard actual scenarios. Additionally, anomalies are treated as salient relative...
When flight vehicles (e.g., aerospace vehicles, Low Earth Orbit (LEO) satellites, near-space aircrafts, Unmanned Aerial Vehicles (UAVs) and drones) fly at high speed, their surfaces suffer the micro-pressure from high-altitude thin air. The long-term effect of this pressure causes surface components vehicle to deform or fall off, which can lead a serious accident. To solve problem, paper proposes sensitivity-compensated flexible sensor based on hyper-elastic plastic material plate parallel...
Forest height is of great significance for forest resource management and carbon sink estimation. Tomographic synthetic aperture radar (TomoSAR) technology provides an effective means the accurate inversion this parameter. Several multi-polarization (SAR) images are generally required to obtain height. However, it common that only a small number single-polarization can be acquired, due complexity systems limitations observation cycles, there may one fully polarimetric image available. This...
Optical remote sensing images are of considerable significance in a plethora applications, including feature recognition and scene semantic segmentation. However, the quality is compromised by influence various types noise, which has detrimental impact on their practical applications aforementioned fields. Furthermore, intricate texture characteristics inherent to present significant hurdle removal noise restoration image details. In order address these challenges, we propose interaction...
Objectives To develop a rapid and low-cost method for 16S rDNA nanopore sequencing. Methods This was prospective study on sequencing method. We developed this barcoding by adding barcodes to the primer reduce reagent cost simplify experimental procedure. Twenty-one common pulmonary bacteria (7 reference strains, 14 clinical isolates) 94 samples of bronchoalveolar lavage fluid from children with severe pneumonia were tested. Results indicating low-abundance pathogenic verified polymerase...
Septic shock as a subset of sepsis, has much higher mortality, while the mechanism is still elusive. This study was aimed at identifying core mechanisms associated with septic and its high mortality by investigating transcriptome data. We screened 72 septic-shock-associated genes (SSAGs) differential expression between sepsis in discovery dataset. Further gene set enrichment analysis identified upregulated neutrophil activation impaired T-cell shock. Co-expression revealed nine co-expressed...
Arbitrarily oriented object detection is one of the most-popular research fields in remote sensing image processing. In this paper, we propose an approach to predict angles indirectly, thereby avoiding issues related angular periodicity and boundary discontinuity. Our method involves representing long edge angle as a vector, which then decompose into horizontal vertical components. By predicting two components can obtain information indirectly. To facilitate transformation between...
Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, creates a gap due to differing optimization objectives. To address this issue, we argue that utilizing pre-trained embeddings derived from process specifically designed optimize informative distinctive helps rank sentences. do so, propose novel graph auto-encoder obtain by...
Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, creates a gap due to differing optimization objectives. To address this issue, we argue that utilizing pre-trained embeddings derived from process specifically designed optimize cohensive distinctive helps rank sentences. do so, propose novel graph auto-encoder obtain by...