Search Results for author:
Found 9 papers, 6 papers with code
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+
HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images
Benefiting from the developments in deep learning technology, deep-learning-based algorithms employing automatic feature extraction have achieved remarkable performance on the change detection (CD) task.
Ranked #1 on
Change Detection
on GoogleGZ-CD
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks.
Ranked #1 on
Semantic Segmentation
on SpaceNet 1
(using extra training data)
Aerial Scene Classification
Building change detection for remote sensing images
+12
Remote Sensing ChatGPT: Solving Remote Sensing Tasks with ChatGPT and Visual Models
Recently, the flourishing large language models(LLM), especially ChatGPT, have shown exceptional performance in language understanding, reasoning, and interaction, attracting users and researchers from multiple fields and domains.
SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images
These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions and 2) the models' confidence in the change regions is inconsistent at different decoder stages.
Building-road Collaborative Extraction from Remotely Sensed Images via Cross-Interaction
Compared with many existing methods that train each task individually, the proposed collaborative extraction method can utilize the complementary advantages between buildings and roads by the proposed inter-task and inter-scale feature interactions, and automatically select the optimal reception field for different tasks.
Expediting Building Footprint Extraction from High-resolution Remote Sensing Images via progressive lenient supervision
The efficacy of building footprint segmentation from remotely sensed images has been hindered by model transfer effectiveness.
DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space
To address these issues, we complement the strong temporal modeling ability of metric learning with the prominent fitting ability of segmentation and propose a deep change feature learning (DeepCL) framework for robust and explainable CD.
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images
The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.
