Search Results for author:
Found 6 papers, 4 papers with code
All grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classification
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.
All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification
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
Local semantic enhanced convnet for aerial scene recognition
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
A multiple-instance densely-connected ConvNet for aerial scene classification
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
Building change detection based on multi-scale filtering and grid partition
Exhaustive experiments indicate that the proposed method can detect building change types directly and outperform the current multi-index learning method.
Multiple instance dense connected convolution neural network for aerial image scene classification
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.
