A5020148486- Infrared Target Detection Methodologies
- Advanced Measurement and Detection Methods
- Remote-Sensing Image Classification
- Video Surveillance and Tracking Methods
- Image and Signal Denoising Methods
- Infrared Thermography in Medicine
- Sparse and Compressive Sensing Techniques
- Neural Networks and Reservoir Computing
- Neural dynamics and brain function
- Advanced Image and Video Retrieval Techniques
- Optical Systems and Laser Technology
- Advanced Memory and Neural Computing
- Remote Sensing and Land Use
- Advanced Neural Network Applications
- Thermography and Photoacoustic Techniques
- Visual Attention and Saliency Detection
- Advanced Image Fusion Techniques
National University of Defense Technology
2022-2024
Shaanxi Normal University
2022
Dalian Maritime University
2021
Hyperspectral anomaly detection (HAD) is regarded as an indispensable, pivotal technology in remote sensing and earth science domains. Nevertheless, most existing approaches for targets flatten 3-D hyperspectral images (HSIs) with spatial spectral information into 2-D vector data, which virtually breaks up the internal structure HSIs degenerates performance. To this end, we directly consider HSI data cube a tensor develop novel low-rank approximation (TLRA) algorithm to separate sparse...
Visible-thermal small object detection (RGBT SOD) is a significant yet challenging task with wide range of applications, including video surveillance, traffic monitoring, search and rescue. However, existing studies mainly focus on either visible or thermal modality, while RGBT SOD rarely explored. Although some datasets have been developed, the insufficient quantity, limited diversity, unitary application, misaligned images large target size cannot provide an impartial benchmark to evaluate...
Compared to hyperspectral trackers that adopt the “pre-training then fine-tuning” training paradigm, those using prompt-tuning” paradigm can inherit expressive capabilities of pre-trained model with fewer parameters. Existing utilizing prompt learning lack an adequate template design, thus failing bridge domain gap between data and models. Consequently, their tracking performance suffers. Additionally, these networks have a poor generalization ability require re-training for different...
When detecting diverse infrared (IR) small maritime targets on complicated scenes, the existing methods get into trouble and unsatisfactory performance. The main reasons are as follows: 1) affected by target characteristics ambient temperature so on, both bright dark may exist IR images in practical application 2) spatial information temporal correlation of not fully excavated. To these problems, we propose a robust anti-jitter spatial–temporal trajectory consistency (ASTTC) method, idea...
Infrared dim small target detection is regarded as a critical technology for the interpretation of space-based remote sensing images. In recent years, driven by deep learning and surge data, remarkable effects have been achieved in infrared Nevertheless, intrinsic feature scarcity low signal-to-clutter ratio (SCR) characteristics pose tremendous challenges to learning-based methods. this letter, we present novel sub-pixel sampling cuneate network (SPSCNet) detect targets The overall model...
With the popularity of snapshot mosaic hyperspectral cameras, researchers have shown great interest in video processing recent years, particularly object tracking. Unlike previous trackers, we train our tracker directly on two-dimensional raw data instead cube data. This allows mainstream RGB trackers to be transferred without any modifications. And same transferer can applied handle from different types sensors. To address issue limited scale existing tracking datasets, convert available...