7 papers
STARS: Shared-specific Translation and Alignment for missing-modality Remote Sensing Semantic Segmentation
Tong Wang, Xiaodong Zhang, Guanzhou Chen +7
Multimodal remote sensing technology significantly enhances the understanding of surface semantics by integrating heterogeneous data such as optical images, Synthetic Aperture Rada…
MSSDF: Modality-Shared Self-supervised Distillation for High-Resolution Multi-modal Remote Sensing Image Learning
Tong Wang, Guanzhou Chen, Xiaodong Zhang +6
Remote sensing image interpretation plays a critical role in environmental monitoring, urban planning, and disaster assessment. However, acquiring high-quality labeled data is ofte…
BFA-YOLO: A balanced multiscale object detection network for building façade attachments detection
Yangguang Chen, Tong Wang, Guanzhou Chen +7
The detection of façade elements on buildings, such as doors, windows, balconies, air conditioning units, billboards, and glass curtain walls, is a critical step in automating the…
S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar Imagery
Qingyuan Yang, Guanzhou Chen, Xiaoliang Tan +3
Stereo matching and semantic segmentation are significant tasks in binocular satellite 3D reconstruction. However, previous studies primarily view these as independent parallel tas…
Segment Change Model (SCM) for Unsupervised Change detection in VHR Remote Sensing Images: a Case Study of Buildings
Xiaoliang Tan, Guanzhou Chen, Tong Wang +2
The field of Remote Sensing (RS) widely employs Change Detection (CD) on very-high-resolution (VHR) images. A majority of extant deep-learning-based methods hinge on annotated samp…
Enhancing Terrestrial Net Primary Productivity Estimation with EXP-CASA: A Novel Light Use Efficiency Model Approach
Guanzhou Chen, Kaiqi Zhang, Xiaodong Zhang +8
The Light Use Efficiency model, epitomized by the CASA model, is extensively applied in the quantitative estimation of vegetation Net Primary Productivity. However, the classic CAS…