A5084825970- Smart Agriculture and AI
- Ocean Waves and Remote Sensing
- Robotics and Sensor-Based Localization
- Soil Carbon and Nitrogen Dynamics
- Remote Sensing and Land Use
- Hydrological Forecasting Using AI
- Speech and Audio Processing
- Energy Load and Power Forecasting
- Remote-Sensing Image Classification
- Indoor and Outdoor Localization Technologies
Kunming University of Science and Technology
2024
China State Shipbuilding (China)
2016
Sichuan University
2005
Remote sensing image semantic segmentation methods have become the main approach for extracting cropland information. However, in mountainous regions of southwestern China, croplands exhibit narrow and fragmented shapes, as well complex planting patterns, making it difficult traditional to accurately delineate fine-grained boundaries. To address these challenges, a multiattention Transformer network named MATNet is proposed this paper, extraction at parcel level scenes. built upon fusion CNN...
To deal with the non-line of sight effect on mobile localization, kernel information surroundings, which can be extracted by supervised learning, is exploited to correct location bias. The proposed kernel-based method takes advantage dimension augment feature space, produced a nonlinear mapping original measurements and ordinary estimate. In sense minimum training error, matrices are learned from set data provided advanced mobiles, locate themselves more accurately coexist old stations....