102 citations · 196 across the 5 of their papers we have counts for
5 papers
Plug-and-Play DISep: Separating Dense Instances for Scene-to-Pixel Weakly-Supervised Change Detection in High-Resolution Remote Sensing Images
Zhenghui Zhao, Chen Wu, Lixiang Ru +3
Existing Weakly-Supervised Change Detection (WSCD) methods often encounter the problem of "instance lumping" under scene-level supervision, particularly in scenarios with a dense d…
C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images
Chengxi Han, Chen Wu, Meiqi Hu +2
A high-precision feature extraction model is crucial for change detection (CD). In the past, many deep learning-based supervised CD methods learned to recognize change feature patt…
LRNet: Change detection of high-resolution remote sensing imagery via strategy of localization-then-refinement
Huan Zhong, Chen Wu, Ziqi Xiao
Change detection, as a research hotspot in the field of remote sensing, has witnessed continuous development and progress. However, the discrimination of boundary details remains a…
Building-road Collaborative Extraction from Remotely Sensed Images via Cross-Interaction
Haonan Guo, Xin Su, Chen Wu +2
Buildings are the basic carrier of social production and human life; roads are the links that interconnect social networks. Building and road information has important application…
Radio Galaxy Zoo: ClaRAN - A Deep Learning Classifier for Radio Morphologies
Chen Wu, O. Ivy Wong, Lawrence Rudnick +13
The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification t…