Search Results for author:
Found 11 papers, 5 papers with code
Low Saturation Confidence Distribution-based Test-Time Adaptation for Cross-Domain Remote Sensing Image Classification
By effectively combining these losses, LSCD-TTA enables rapid and accurate adaptation to the target domain for RS image classification.
Evidential Graph Contrastive Alignment for Source-Free Blending-Target Domain Adaptation
In this paper, we firstly tackle a more realistic Domain Adaptation (DA) setting: Source-Free Blending-Target Domain Adaptation (SF-BTDA), where we can not access to source domain data while facing mixed multiple target domains without any domain labels in prior.
Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments
We demonstrate the effectiveness of AMROD on four CTTA object detection tasks, where AMROD outperforms existing methods, especially achieving a 3. 2 mAP improvement and a 20% increase in efficiency on the Cityscapes-to-Cityscapes-C CTTA task.
A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder
A$^{2}$-MAE integrates an anchor-aware masking strategy and a geographic encoding module to comprehensively exploit the properties of RS images.
FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding
Although there have been advances in high-quality land cover datasets that reveal the physical features of urban landscapes, the lack of fine-grained land use datasets hinders a deeper understanding of how human activities are distributed across the landscape and the impact of these activities on the environment, thus constraining proper technique development.
3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisions
3D building reconstruction from monocular remote sensing images is an important and challenging research problem that has received increasing attention in recent years, owing to its low cost of data acquisition and availability for large-scale applications.
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model
Specifically, we inject the priors into the denoising model to improve the utilization of reference information in unchanged areas and regulate the reconstruction of semantically relevant content in changed areas.
DeepLight: Reconstructing High-Resolution Observations of Nighttime Light With Multi-Modal Remote Sensing Data
Nighttime light (NTL) remote sensing observation serves as a unique proxy for quantitatively assessing progress toward meeting a series of Sustainable Development Goals (SDGs), such as poverty estimation, urban sustainable development, and carbon emission.
A review of individual tree crown detection and delineation from optical remote sensing images
); (2) traditional machine learning methods (such as random forest, decision tree, etc.
Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation
Moreover, we propose the Class Center Contrast method to jointly utilize the labeled and unlabeled data.
Cross-regional oil palm tree counting and detection via multi-level attention domain adaptation network
In this paper, we propose a novel domain adaptive oil palm tree detection method, i. e., a Multi-level Attention Domain Adaptation Network (MADAN) to reap cross-regional oil palm tree counting and detection.
