Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images

Gabriele Cavallaro; Yakoub Bazi; Farid Melgani; Morris Riedel

doi:10.1109/IGARSS.2019.8899831

Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images

Gabriele Cavallaro, Yakoub Bazi, Farid Melgani, Morris Riedel

Faculty of Industrial Engineering, Mechanical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The classification of land-cover classes in remote sensing images can suit a variety of interdisciplinary applications such as the interpretation of natural and man-made processes on the Earth surface. The Convolutional Support Vector Machine (CSVM) network was recently proposed as binary classifier for the detection of objects in Unmanned Aerial Vehicle (UAV) images. The training phase of the CSVM is based on convolutional layers that learn the kernel weights via a set of linear Support Vector Machines (SVMs). This paper proposes the Multi-scale Convolutional Support Vector Machine (MCSVM) network, that is an ensemble of CSVM classifiers which process patches of different spatial sizes and can deal with multi-class classification problems. The experiments are carried out on the EuroSAT Sentinel-2 dataset and the results are compared to the one obtained with recent transfer learning approaches based on pre-trained Convolutional Neural Networks (CNNs).

Original language	English
Title of host publication	2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	875-878
Number of pages	4
ISBN (Electronic)	9781538691540
DOIs	https://doi.org/10.1109/IGARSS.2019.8899831
Publication status	Published - Jul 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Other keywords

Multi-scale convolutional support vector machine (MCSVM) network
multiclass classification
remote sensing
sentinel-2
supervised feature generation

Access to Document

10.1109/IGARSS.2019.8899831

Cite this

Cavallaro, G., Bazi, Y., Melgani, F., & Riedel, M. (2019). Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 875-878). [8899831] (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2019.8899831

Cavallaro, Gabriele ; Bazi, Yakoub ; Melgani, Farid et al. / Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images. 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 875-878 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images",

abstract = "The classification of land-cover classes in remote sensing images can suit a variety of interdisciplinary applications such as the interpretation of natural and man-made processes on the Earth surface. The Convolutional Support Vector Machine (CSVM) network was recently proposed as binary classifier for the detection of objects in Unmanned Aerial Vehicle (UAV) images. The training phase of the CSVM is based on convolutional layers that learn the kernel weights via a set of linear Support Vector Machines (SVMs). This paper proposes the Multi-scale Convolutional Support Vector Machine (MCSVM) network, that is an ensemble of CSVM classifiers which process patches of different spatial sizes and can deal with multi-class classification problems. The experiments are carried out on the EuroSAT Sentinel-2 dataset and the results are compared to the one obtained with recent transfer learning approaches based on pre-trained Convolutional Neural Networks (CNNs).",

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Cavallaro, G, Bazi, Y, Melgani, F & Riedel, M 2019, Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images. in 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings., 8899831, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 875-878, 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/IGARSS.2019.8899831

Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images. / Cavallaro, Gabriele; Bazi, Yakoub; Melgani, Farid et al.

2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 875-878 8899831 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Cavallaro G, Bazi Y, Melgani F, Riedel M. Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 875-878. 8899831. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2019.8899831

Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images

Abstract

Publication series

Conference

Bibliographical note

Other keywords

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Cite this