A5063017060- Remote-Sensing Image Classification
- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
- Remote Sensing and Land Use
- Fire Detection and Safety Systems
- Anomaly Detection Techniques and Applications
- Remote Sensing in Agriculture
Shaanxi University of Science and Technology
2022-2023
Shanxi University
2022
Deep convolutional neural networks have achieved much success in remote sensing image change detection (CD) but still suffer from two main problems. First, existing multi-scale feature fusion methods often employ redundant extraction and strategies, which leads to high computational costs memory usage. Second, the regular attention mechanism CD is difficult model spatial-spectral features generate 3D weights at same time, ignoring cooperation between spatial spectral features. To address...
The popular convolutional neural networks (CNNs) have been successfully used in very high-resolution remote sensing image semantic segmentation. However, these often suffer from performance limitations. First, although deeper usually provide better feature representation, they may cause parameter redundancy and the inefficient use of prior knowledge. Secondly, attention-based only focus on weighting different features a single sample but ignore correlation all samples training set, thus...