Fully Convolutional Networks for Multi-temporal SAR Image Classification

A.G. Mullissa; C. Persello; V.A. Tolpekin

doi:10.1109/IGARSS.2018.8518780

Fully Convolutional Networks for Multi-temporal SAR Image Classification

A.G. Mullissa, C. Persello, V.A. Tolpekin

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

29 Citations (Scopus)

23 Downloads (Pure)

Abstract

Classification of crop types from multi-temporal SAR data is a complex task because of the need to extract spatial and temporal features from images affected by speckle. Previous methods applied speckle filtering and then classification in
two separate processing steps. This paper introduces fully convolutional networks (FCN) for pixel-wise classification of crops from multi-temporal SAR data. It applies speckle filtering and classification in a single framework. Furthermore, it also uses dilated kernels to increase the capability to learn long distance spatial dependencies. The proposed FCN was compared with a patch-based convolutional neural network (CNN) and support vector machine (SVM) classifiers. The proposed method performed better when compared with the patch-based CNN and SVM.

Original language	English
Title of host publication	IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Publisher	IEEE
Pages	6635-3338
Number of pages	4
ISBN (Electronic)	978-1-5386-7150-4
DOIs	https://doi.org/10.1109/IGARSS.2018.8518780
Publication status	Published - 5 Nov 2018
Event	38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018: Observing, Understanding and Forcasting the Dynamics of Our Planet - Feria Valencia Convention & Exhibition Center, Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018 Conference number: 38 https://www.igarss2018.org/

Conference

Conference	38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Abbreviated title	2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18
Internet address	https://www.igarss2018.org/

Keywords

Deep learning
Fully convolutional networks
Remote Sensing
SAR
Sentinel-1

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10.1109/IGARSS.2018.8518780

Cite this

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abstract = "Classification of crop types from multi-temporal SAR data is a complex task because of the need to extract spatial and temporal features from images affected by speckle. Previous methods applied speckle filtering and then classification intwo separate processing steps. This paper introduces fully convolutional networks (FCN) for pixel-wise classification of crops from multi-temporal SAR data. It applies speckle filtering and classification in a single framework. Furthermore, it also uses dilated kernels to increase the capability to learn long distance spatial dependencies. The proposed FCN was compared with a patch-based convolutional neural network (CNN) and support vector machine (SVM) classifiers. The proposed method performed better when compared with the patch-based CNN and SVM.",

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author = "A.G. Mullissa and C. Persello and V.A. Tolpekin",

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Mullissa, AG, Persello, C & Tolpekin, VA 2018, Fully Convolutional Networks for Multi-temporal SAR Image Classification. in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium., 8518780, IEEE, pp. 6635-3338, 38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518780

TY - CHAP

T1 - Fully Convolutional Networks for Multi-temporal SAR Image Classification

AU - Mullissa, A.G.

AU - Persello, C.

AU - Tolpekin, V.A.

N1 - Conference code: 38

PY - 2018/11/5

Y1 - 2018/11/5

N2 - Classification of crop types from multi-temporal SAR data is a complex task because of the need to extract spatial and temporal features from images affected by speckle. Previous methods applied speckle filtering and then classification intwo separate processing steps. This paper introduces fully convolutional networks (FCN) for pixel-wise classification of crops from multi-temporal SAR data. It applies speckle filtering and classification in a single framework. Furthermore, it also uses dilated kernels to increase the capability to learn long distance spatial dependencies. The proposed FCN was compared with a patch-based convolutional neural network (CNN) and support vector machine (SVM) classifiers. The proposed method performed better when compared with the patch-based CNN and SVM.

AB - Classification of crop types from multi-temporal SAR data is a complex task because of the need to extract spatial and temporal features from images affected by speckle. Previous methods applied speckle filtering and then classification intwo separate processing steps. This paper introduces fully convolutional networks (FCN) for pixel-wise classification of crops from multi-temporal SAR data. It applies speckle filtering and classification in a single framework. Furthermore, it also uses dilated kernels to increase the capability to learn long distance spatial dependencies. The proposed FCN was compared with a patch-based convolutional neural network (CNN) and support vector machine (SVM) classifiers. The proposed method performed better when compared with the patch-based CNN and SVM.

KW - Deep learning

KW - Fully convolutional networks

KW - Remote Sensing

KW - SAR

KW - Sentinel-1

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BT - IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

PB - IEEE

T2 - 38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

Y2 - 22 July 2018 through 27 July 2018

ER -

Fully Convolutional Networks for Multi-temporal SAR Image Classification

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Keywords

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