A5020487405- Image Retrieval and Classification Techniques
- Remote Sensing in Agriculture
- Remote-Sensing Image Classification
- Advanced Image and Video Retrieval Techniques
- Video Surveillance and Tracking Methods
- Advanced Image Fusion Techniques
- Smart Agriculture and AI
- Remote Sensing and LiDAR Applications
Northeast Forestry University
2022-2024
The spatiotemporal remote sensing images have a significant importance in forest ecological monitoring, carbon management, and other related fields. Spatiotemporal data fusion technology of combines high temporal resolution to address the current limitation single sensors obtaining resolution. This has gained widespread attention recent years. However, models still exhibit some shortcomings dealing with land cover changes, such as poor clustering results, inaccurate incremental calculations,...
Deep unsupervised hashing methods are gaining attention in the field of remote sensing (RS) image retrieval due to rapid growth volume unlabeled RS data. Most previous research used only natural image-based pre-trained models generate label matrices; however, this method cannot capture semantic information images well and limits accuracy retrieval. To solve problem, authors propose a deep weighted (DUWH) model that uses similarity matrix updating strategy based on structure achieve mutual...
Abstract Pine wood nematode infection is a devastating disease. Unmanned aerial vehicle (UAV) remote sensing enables timely and precise monitoring. However, UAV images are challenged by small target size complex surface backgrounds which hinder their effectiveness in To address these challenges, based on the analysis optimization of images, this study developed spatio-temporal multi-scale fusion algorithm for disease detection. The multi-head, self-attention mechanism incorporated to issue...