295 citations · 955 across the 16 of their papers we have counts for
11 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…
MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification with Zoom-Free Remote Sensing Imagery
Yansheng Li, Yuning Wu, Gong Cheng +9
Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications. However, existing approaches often rely on manuall…
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…
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…
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images
Haifeng Li, Yi Li, Guo Zhang +4
Supervised learning for semantic segmentation requires a large number of labeled samples, which is difficult to obtain in the field of remote sensing. Self-supervised learning (SSL…
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…
RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification
Haifeng Li, Zhenqi Cui, Zhiqing Zhu +4
Training a modern deep neural network on massive labeled samples is the main paradigm in solving the scene classification problem for remote sensing, but learning from only a few d…
SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images
Haifeng Li, Kaijian Qiu, Li Chen +3
High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an impor…
On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition
Jie Chen, Chao Yuan, Min Deng +3
Human vision possesses strong invariance in image recognition. The cognitive capability of deep convolutional neural network (DCNN) is close to the human visual level because of hi…
What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?
Haifeng Li, Jian Peng, Chao Tao +2
Recently, deep convolutional neural network (DCNN) achieved increasingly remarkable success and rapidly developed in the field of natural image recognition. Compared with the natur…