102 citations · 116 across the 7 of their papers we have counts for
7 papers
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…
SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection
Ji Qi, Xinchang Zhang, Dingqi Ye +4
The rapid advancement of generative artificial intelligence is producing fake remote sensing imagery (RSI) that is increasingly difficult to detect, potentially leading to erroneou…
Weakly Supervised Framework Considering Multi-temporal Information for Large-scale Cropland Mapping with Satellite Imagery
Yuze Wang, Aoran Hu, Ji Qi +2
Accurately mapping large-scale cropland is crucial for agricultural production management and planning. Currently, the combination of remote sensing data and deep learning techniqu…
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…
TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning
Chao Tao, Ji Qia, Guo Zhang +3
Do we on the right way for remote sensing image understanding (RSIU) by training models via supervised data-dependent and task-dependent way, instead of human vision in a label-fre…
Sim2Real Object-Centric Keypoint Detection and Description
Chengliang Zhong, Chao Yang, Jinshan Qi +4
Keypoint detection and description play a central role in computer vision. Most existing methods are in the form of scene-level prediction, without returning the object classes of…
Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples
Chao Tao, Ji Qi, Weipeng Lu +2
With the development of deep learning, supervised learning methods perform well in remote sensing images (RSIs) scene classification. However, supervised learning requires a huge n…