1 code implementation • IEEE Transactions on Geoscience and Remote Sensing 2022 • Qi Bi, Beichen Zhou, Kun Qin, Qinghao Ye, Gui-Song Xia
Finally, our SSF module allows our framework to learn the same scene scheme from multigrain instance representations and fuses them, so that the entire framework is optimized as a whole.
1 code implementation • IEEE Transactions on Geoscience and Remote Sensing 2022 • Qi Bi, Beichen Zhou, Kun Qin, Qinghao Ye, Gui-Song Xia
Finally, our SSF allows our framework to learn the same scene scheme from multi-grain instance representations and fuses them, so that the entire framework is optimized as a whole.
Ranked #1 on Scene Recognition on AID
1 code implementation • IEEE Transactions on Image Processing 2021 • Qi Bi, Kun Qin, Han Zhang, Gui-Song Xia
Our LSE-Net consists of a context enhanced convolutional feature extractor, a local semantic perception module and a classification layer.
Ranked #2 on Scene Recognition on AID
1 code implementation • IEEE Transactions on Image Processing 2020 • Qi Bi, Kun Qin, Zhili Li, Han Zhang, Kai Xu, Gui-Song Xia
It regards aerial scene classification as a multiple-instance learning problem so that local semantics can be further investigated.
Ranked #3 on Scene Recognition on AID
no code implementations • 22 Aug 2019 • Qi Bi, Kun Qin, Han Zhang, Wenjun Han, Zhili Li, Kai Xu
Exhaustive experiments indicate that the proposed method can detect building change types directly and outperform the current multi-index learning method.
no code implementations • 22 Aug 2019 • Qi Bi, Kun Qin, Zhili Li, Han Zhang, Kai Xu
While the current convolution neural network tends to extract global features and global semantic information in a scene, the geo-spatial objects can be located at anywhere in an aerial image scene and their spatial arrangement tends to be more complicated.