102 citations · 151 across the 9 of their papers we have counts for
6 papers · 1 filter
Learning the Target Priors Before Image Translation: A Decoupled Training Paradigm for Cross-Modal Image Translation in Remote Sensing
Keyan Hu, Mingtao Wang, Ziyu Zhou +4
Cross-modal image translation in remote sensing must preserve source-observed content while matching the target-domain distribution. Existing methods jointly learn the target prior…
SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameters
Jiahao Cui, Jiale Duan, Binyan Luo +3
Deep neural network-based Synthetic Aperture Radar (SAR) target recognition models are susceptible to adversarial examples. Current adversarial example generation methods for SAR i…
Using Global Land Cover Product as Prompt for Cropland Mapping via Visual Foundation Model
Chao Tao, Aoran Hu, Rong Xiao +2
Data-driven deep learning methods have shown great potential in cropland mapping. However, due to multiple factors such as attributes of cropland (topography, climate, crop type) a…
BCE-Net: Reliable Building Footprints Change Extraction based on Historical Map and Up-to-Date Images using Contrastive Learning
Cheng Liao, Han Hu, Xuekun Yuan +6
Automatic and periodic recompiling of building databases with up-to-date high-resolution images has become a critical requirement for rapidly developing urban environments. However…
Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works
Chao Tao, Ji Qi, Mingning Guo +2
Deep learning has achieved great success in learning features from massive remote sensing images (RSIs). To better understand the connection between feature learning paradigms (e.g…
False: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image
Zhaoyang Zhang, Xuying Wang, Xiaoming Mei +2
The existing SSCL of RSI is built based on constructing positive and negative sample pairs. However, due to the richness of RSI ground objects and the complexity of the RSI context…