Browse by author
Lookup NU author(s): Dr Shidong WangORCiD, Dr Yu GuanORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
© 1980-2012 IEEE. Learning discriminative and invariant feature representation is the key to visual image categorization. In this article, we propose a novel invariant deep compressible covariance pooling (IDCCP) to solve nuisance variations in aerial scene categorization. We consider transforming the input image according to a finite transformation group that consists of multiple confounding orthogonal matrices, such as the D4 group. Then, we adopt a Siamese-style network to transfer the group structure to the representation space, where we can derive a trivial representation that is invariant under the group action. The linear classifier trained with trivial representation will also be possessed with invariance. To further improve the discriminative power of representation, we extend the representation to the tensor space while imposing orthogonal constraints on the transformation matrix to effectively reduce feature dimensions. We conduct extensive experiments on the publicly released aerial scene image data sets and demonstrate the superiority of this method compared with state-of-the-art methods. In particular, with using ResNet architecture, our IDCCP model can reduce the dimension of the tensor representation by about 98% without sacrificing accuracy (i.e., < 0.5%).
Author(s): Wang S, Ren Y, Parr G, Guan Y, Shao L
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Geoscience and Remote Sensing
Year: 2021
Volume: 59
Issue: 8
Pages: 6549-6561
Print publication date: 01/08/2021
Online publication date: 02/10/2020
Acceptance date: 15/09/2020
ISSN (print): 0196-2892
ISSN (electronic): 1558-0644
Publisher: IEEE
URL: https://doi.org/10.1109/TGRS.2020.3026221
DOI: 10.1109/TGRS.2020.3026221
Altmetrics provided by Altmetric
