Remote Sensing Scene Classification Based on Covariance Pooling of Multi-layer CNN Features Guided by Saliency Maps
| dc.rights.license | open | en_US |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | AKODAD, Sara | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | BOMBRUN, Lionel
ORCID: 0000-0001-9036-3988 IDREF: 137837461 | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | GERMAIN, Christian
ORCID: 0000-0002-3097-8283 IDREF: 130936634 | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | BERTHOUMIEU, Yannick | |
| dc.date.accessioned | 2022-09-02T13:56:37Z | |
| dc.date.available | 2022-09-02T13:56:37Z | |
| dc.date.issued | 2022-01-01 | |
| dc.identifier.isbn | 978-3-031-09037-0 | en_US |
| dc.identifier.uri | oai:crossref.org:10.1007/978-3-031-09037-0_47 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/140641 | |
| dc.description.abstractEn | The new generation of remote sensing imaging sensors enables high spatial, spectral and temporal resolution images with high revisit frequencies. These sensors allow the acquisition of multi-spectral and multi-temporal images. The availability of these data has raised the interest of the remote sensing community to develop novel machine learning strategies for supervised classification. This paper aims at introducing a novel supervised classification algorithm based on covariance pooling of multi-layer convolutional neural network (CNN) features. The basic idea consists in an ensemble learning approach based on covariance matrices estimation from CNN features. Then, after being projected on the log-Euclidean space, an SVM classifier is used to make a decision. In order to give more strength to relatively small objects of interest in the scene, we propose to incorporate the visual saliency map in the process. For that, inspired by the theory of robust statistics, a weighted covariance matrix estimator is considered. Larger weights are given to more salient regions. Finally, some experiments on remote sensing classification are conducted on the UC Merced land use dataset. The obtained results confirm the potential of the proposed approach in terms of classification scene accuracy. It demonstrates, besides the interest of exploiting second order statistics and adopting an ensemble learning approach, the benefit of incorporating visual saliency maps. | |
| dc.language.iso | EN | en_US |
| dc.publisher | Springer International Publishing | en_US |
| dc.source | crossref | |
| dc.source.title | Pattern Recognition and Artificial Intelligence. ICPRAI 2022 | en_US |
| dc.subject.en | Covariance pooling | |
| dc.subject.en | Saliency map | |
| dc.subject.en | Ensemble learning | |
| dc.subject.en | Multi-layer CNN features | |
| dc.title.en | Remote Sensing Scene Classification Based on Covariance Pooling of Multi-layer CNN Features Guided by Saliency Maps | |
| dc.type | Chapitre d'ouvrage | en_US |
| dc.identifier.doi | 10.1007/978-3-031-09037-0_47 | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Automatique / Robotique | en_US |
| dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | en_US |
| bordeaux.page | 579-590 | en_US |
| bordeaux.hal.laboratories | Laboratoire d’Intégration du Matériau au Système (IMS) - UMR 5218 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | Bordeaux INP | en_US |
| bordeaux.institution | CNRS | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | dissemin | |
| hal.identifier | hal-03768102 | |
| hal.version | 1 | |
| hal.date.transferred | 2022-09-02T13:56:39Z | |
| hal.export | true | |
| workflow.import.source | dissemin | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Pattern%20Recognition%20and%20Artificial%20Intelligence.%20ICPRAI%202022&rft.date=2022-01-01&rft.spage=579-590&rft.epage=579-590&rft.au=AKODAD,%20Sara&BOMBRUN,%20Lionel&GERMAIN,%20Christian&BERTHOUMIEU,%20Yannick&rft.isbn=978-3-031-09037-0&rft.genre=unknown |
Fichier(s) constituant ce document
| Fichiers | Taille | Format | Vue |
|---|---|---|---|
|
Il n'y a pas de fichiers associés à ce document. |
|||