Search Results for author:
Found 7 papers, 5 papers with code
Identifying every building's function in large-scale urban areas with multi-modality remote-sensing data
In this study, we proposed a semi-supervised framework to identify every building's function in large-scale urban areas with multi-modality remote-sensing data.
C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images
A high-precision feature extraction model is crucial for change detection (CD).
Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery
The rapid advancement of automated artificial intelligence algorithms and remote sensing instruments has benefited change detection (CD) tasks.
Ranked #1 on
Change Detection
on LEVIR+
Learning without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical Labels
However, it is still a non-trivial task hindered by complex ground details, various landforms, and the scarcity of accurate training labels over a wide-span geographic area.
Cross-level Attention with Overlapped Windows for Camouflaged Object Detection
In this paper, we propose an overlapped window cross-level attention (OWinCA) to achieve the low-level feature enhancement guided by the highest-level features.
Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network
Building extraction aims to segment building pixels from remote sensing images and plays an essential role in many applications, such as city planning and urban dynamic monitoring.
Ranked #1 on
Semantic Segmentation
on INRIA Aerial Image Labeling
Towards Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentation
In Stage A, the first branch decoder of the difference-aware decoder is used to obtain a guide map.
