Urban change detection for multispectral earth observation using convolutional neural networks

Rodrigo Caye Daudt; Bertrand Le Saux; Alexandre Boulch; Yann Gousseau

doi:10.1109/IGARSS.2018.8518015

Urban change detection for multispectral earth observation using convolutional neural networks

Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau

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

Abstract

The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2115-2118
Number of pages	4
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8518015
Publication status	Published - 31 Oct 2018
Externally published	Yes
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2018-July

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18

Keywords

Change detection
Convolutional neural networks
Multispectral earth observation
Supervised machine learning

Access to Document

10.1109/IGARSS.2018.8518015

Cite this

Daudt, R. C., Le Saux, B., Boulch, A., & Gousseau, Y. (2018). Urban change detection for multispectral earth observation using convolutional neural networks. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 2115-2118). Article 8518015 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8518015

Daudt, Rodrigo Caye ; Le Saux, Bertrand ; Boulch, Alexandre et al. / Urban change detection for multispectral earth observation using convolutional neural networks. 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2115-2118 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "Urban change detection for multispectral earth observation using convolutional neural networks",

abstract = "The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.",

keywords = "Change detection, Convolutional neural networks, Multispectral earth observation, Supervised machine learning",

author = "Daudt, \{Rodrigo Caye\} and \{Le Saux\}, Bertrand and Alexandre Boulch and Yann Gousseau",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",

year = "2018",

month = oct,

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doi = "10.1109/IGARSS.2018.8518015",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Daudt, RC, Le Saux, B, Boulch, A & Gousseau, Y 2018, Urban change detection for multispectral earth observation using convolutional neural networks. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8518015, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 2115-2118, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518015

Urban change detection for multispectral earth observation using convolutional neural networks. / Daudt, Rodrigo Caye; Le Saux, Bertrand; Boulch, Alexandre et al.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2115-2118 8518015 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

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

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N2 - The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.

AB - The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.

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Daudt RC, Le Saux B, Boulch A, Gousseau Y. Urban change detection for multispectral earth observation using convolutional neural networks. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2115-2118. 8518015. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2018.8518015

Urban change detection for multispectral earth observation using convolutional neural networks

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