Deep Learning Based Patch-Wise Land Cover Land Use Classification: A New Small Benchmark Sentinel-2 Image Dataset

Gulsan Alp; Elif Sertel

doi:10.1109/IGARSS46834.2022.9883715

Deep Learning Based Patch-Wise Land Cover Land Use Classification: A New Small Benchmark Sentinel-2 Image Dataset

Gulsan Alp, Elif Sertel

Department of Geomatics Engineering

Istanbul Technical University

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

3 Citations (Scopus)

Abstract

In this paper, patch-wise land cover and land use (LCLU) classification was performed using the state-of-art ResNet 50 and Inception-ResNet-V2 architecture trained with Stochastic Gradient Descent(SGD) and Nadam optimizers. A new dataset was generated for the classification task using Sentinel-2 images having different patch sizes. The image patches were labeled using CORINE Land Cover (CLC) 2018 map. The dataset has 1961 image patches and it was divided into 1397 training and 564 testing patches during the experiment. Our dataset contains samples labeled with 7 CLC Level-2 classes. While the best training accuracy of 98.0% was obtained by Inception-ResNet-V2 trained with Nadam. The best testing accuracy of 93.0% was achieved with Inception-ResNet-V2 by using SGD optimizer.

Original language	English
Title of host publication	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3179-3182
Number of pages	4
ISBN (Electronic)	9781665427920
DOIs	https://doi.org/10.1109/IGARSS46834.2022.9883715
Publication status	Published - 2022
Event	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2022-July

Conference

Conference	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/07/22 → 22/07/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

CORINE (CLC)
Deep Learning Classification
Land Cover
Land Use
Remote Sensing
Satellite Image Dataset
Sentinel-2

Access to Document

10.1109/IGARSS46834.2022.9883715

Cite this

Alp, G., & Sertel, E. (2022). Deep Learning Based Patch-Wise Land Cover Land Use Classification: A New Small Benchmark Sentinel-2 Image Dataset. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 3179-3182). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS46834.2022.9883715

@inproceedings{a2dee8a2353a49c693259ff98e3f1844,

title = "Deep Learning Based Patch-Wise Land Cover Land Use Classification: A New Small Benchmark Sentinel-2 Image Dataset",

abstract = "In this paper, patch-wise land cover and land use (LCLU) classification was performed using the state-of-art ResNet 50 and Inception-ResNet-V2 architecture trained with Stochastic Gradient Descent(SGD) and Nadam optimizers. A new dataset was generated for the classification task using Sentinel-2 images having different patch sizes. The image patches were labeled using CORINE Land Cover (CLC) 2018 map. The dataset has 1961 image patches and it was divided into 1397 training and 564 testing patches during the experiment. Our dataset contains samples labeled with 7 CLC Level-2 classes. While the best training accuracy of 98.0% was obtained by Inception-ResNet-V2 trained with Nadam. The best testing accuracy of 93.0% was achieved with Inception-ResNet-V2 by using SGD optimizer.",

keywords = "CORINE (CLC), Deep Learning Classification, Land Cover, Land Use, Remote Sensing, Satellite Image Dataset, Sentinel-2",

author = "Gulsan Alp and Elif Sertel",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",

year = "2022",

doi = "10.1109/IGARSS46834.2022.9883715",

language = "English",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3179--3182",

booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",

address = "United States",

}

Alp, G & Sertel, E 2022, Deep Learning Based Patch-Wise Land Cover Land Use Classification: A New Small Benchmark Sentinel-2 Image Dataset. in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 3179-3182, 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, Kuala Lumpur, Malaysia, 17/07/22. https://doi.org/10.1109/IGARSS46834.2022.9883715

Deep Learning Based Patch-Wise Land Cover Land Use Classification: A New Small Benchmark Sentinel-2 Image Dataset. / Alp, Gulsan; Sertel, Elif.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3179-3182 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July).

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

TY - GEN

T1 - Deep Learning Based Patch-Wise Land Cover Land Use Classification

T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022

AU - Alp, Gulsan

AU - Sertel, Elif

PY - 2022

Y1 - 2022

N2 - In this paper, patch-wise land cover and land use (LCLU) classification was performed using the state-of-art ResNet 50 and Inception-ResNet-V2 architecture trained with Stochastic Gradient Descent(SGD) and Nadam optimizers. A new dataset was generated for the classification task using Sentinel-2 images having different patch sizes. The image patches were labeled using CORINE Land Cover (CLC) 2018 map. The dataset has 1961 image patches and it was divided into 1397 training and 564 testing patches during the experiment. Our dataset contains samples labeled with 7 CLC Level-2 classes. While the best training accuracy of 98.0% was obtained by Inception-ResNet-V2 trained with Nadam. The best testing accuracy of 93.0% was achieved with Inception-ResNet-V2 by using SGD optimizer.

AB - In this paper, patch-wise land cover and land use (LCLU) classification was performed using the state-of-art ResNet 50 and Inception-ResNet-V2 architecture trained with Stochastic Gradient Descent(SGD) and Nadam optimizers. A new dataset was generated for the classification task using Sentinel-2 images having different patch sizes. The image patches were labeled using CORINE Land Cover (CLC) 2018 map. The dataset has 1961 image patches and it was divided into 1397 training and 564 testing patches during the experiment. Our dataset contains samples labeled with 7 CLC Level-2 classes. While the best training accuracy of 98.0% was obtained by Inception-ResNet-V2 trained with Nadam. The best testing accuracy of 93.0% was achieved with Inception-ResNet-V2 by using SGD optimizer.

KW - CORINE (CLC)

KW - Deep Learning Classification

KW - Land Cover

KW - Land Use

KW - Remote Sensing

KW - Satellite Image Dataset

KW - Sentinel-2

UR - http://www.scopus.com/inward/record.url?scp=85140405581&partnerID=8YFLogxK

U2 - 10.1109/IGARSS46834.2022.9883715

DO - 10.1109/IGARSS46834.2022.9883715

M3 - Conference contribution

AN - SCOPUS:85140405581

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 3179

EP - 3182

BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 17 July 2022 through 22 July 2022

ER -

Alp G, Sertel E. Deep Learning Based Patch-Wise Land Cover Land Use Classification: A New Small Benchmark Sentinel-2 Image Dataset. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc. 2022. p. 3179-3182. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS46834.2022.9883715

Deep Learning Based Patch-Wise Land Cover Land Use Classification: A New Small Benchmark Sentinel-2 Image Dataset

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this