98 citations · 134 across the 6 of their papers we have counts for
7 papers
AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting
Jiawei Zhu, Chao Tao, Hanhan Deng +4
Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow informati…
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…