Deep Learning for Agricultural Land Detection in Insular Areas

Eleni Charou; George Felekis; Danai Bournou Stavroulopoulou; Maria Koutsoukou; Antigoni Panagiotopoulou; Yorghos Voutos; Emmanuel Bratsolis; Phivos Mylonas; Laurence Likforman-Sulem

doi:10.1109/IISA.2019.8900670

Deep Learning for Agricultural Land Detection in Insular Areas

Eleni Charou, George Felekis, Danai Bournou Stavroulopoulou, Maria Koutsoukou, Antigoni Panagiotopoulou, Yorghos Voutos, Emmanuel Bratsolis, Phivos Mylonas, Laurence Likforman-Sulem

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

Abstract

Nowadays, governmental programs like ESA's Copernicus provide freely available data that can be easily utilized for earth observation. In the present work, the problem of detecting agricultural and non-agricultural land cover is addressed. The methodology is based on classification with convolutional neural networks (CNNs) and transfer learning using AlexNet. The study area is located at the Ionian Islands, which include several land cover classes according to Copernicus CORINE Land Cover 2018 (CLC 2018). Furthermore, the dataset consists of natural color images acquired by Sentinel-2A multi-spectral instrument. Experimentation proves that extra addition of training data from foreign grounds, unfamiliar to the Greek data, serves much as a confusing agent regarding network performance.

Original language	English
Title of host publication	10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728149592
DOIs	https://doi.org/10.1109/IISA.2019.8900670
Publication status	Published - 1 Jul 2019
Externally published	Yes
Event	10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 - Patras, Greece Duration: 15 Jul 2019 → 17 Jul 2019

Publication series

Name	10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019

Conference

Conference	10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
Country/Territory	Greece
City	Patras
Period	15/07/19 → 17/07/19

Keywords

Agricultural land
CORINE database
Deep Convolutional Neural Networks
Deep learning
Ionian Sea
Remote Sensing
Satellite Image Classification
Transfer learning

Access to Document

10.1109/IISA.2019.8900670

Cite this

Charou, E., Felekis, G., Stavroulopoulou, D. B., Koutsoukou, M., Panagiotopoulou, A., Voutos, Y., Bratsolis, E., Mylonas, P., & Likforman-Sulem, L. (2019). Deep Learning for Agricultural Land Detection in Insular Areas. In 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 Article 8900670 (10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IISA.2019.8900670

Charou, Eleni ; Felekis, George ; Stavroulopoulou, Danai Bournou et al. / Deep Learning for Agricultural Land Detection in Insular Areas. 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019).

@inproceedings{db910979b3654f588343ba6173a6ed7b,

title = "Deep Learning for Agricultural Land Detection in Insular Areas",

abstract = "Nowadays, governmental programs like ESA's Copernicus provide freely available data that can be easily utilized for earth observation. In the present work, the problem of detecting agricultural and non-agricultural land cover is addressed. The methodology is based on classification with convolutional neural networks (CNNs) and transfer learning using AlexNet. The study area is located at the Ionian Islands, which include several land cover classes according to Copernicus CORINE Land Cover 2018 (CLC 2018). Furthermore, the dataset consists of natural color images acquired by Sentinel-2A multi-spectral instrument. Experimentation proves that extra addition of training data from foreign grounds, unfamiliar to the Greek data, serves much as a confusing agent regarding network performance.",

keywords = "Agricultural land, CORINE database, Deep Convolutional Neural Networks, Deep learning, Ionian Sea, Remote Sensing, Satellite Image Classification, Transfer learning",

author = "Eleni Charou and George Felekis and Stavroulopoulou, \{Danai Bournou\} and Maria Koutsoukou and Antigoni Panagiotopoulou and Yorghos Voutos and Emmanuel Bratsolis and Phivos Mylonas and Laurence Likforman-Sulem",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 ; Conference date: 15-07-2019 Through 17-07-2019",

year = "2019",

month = jul,

day = "1",

doi = "10.1109/IISA.2019.8900670",

language = "English",

series = "10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019",

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

booktitle = "10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019",

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Charou, E, Felekis, G, Stavroulopoulou, DB, Koutsoukou, M, Panagiotopoulou, A, Voutos, Y, Bratsolis, E, Mylonas, P & Likforman-Sulem, L 2019, Deep Learning for Agricultural Land Detection in Insular Areas. in 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019., 8900670, 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019, Institute of Electrical and Electronics Engineers Inc., 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019, Patras, Greece, 15/07/19. https://doi.org/10.1109/IISA.2019.8900670

Deep Learning for Agricultural Land Detection in Insular Areas. / Charou, Eleni; Felekis, George; Stavroulopoulou, Danai Bournou et al.
10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8900670 (10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019).

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

TY - GEN

T1 - Deep Learning for Agricultural Land Detection in Insular Areas

AU - Charou, Eleni

AU - Felekis, George

AU - Stavroulopoulou, Danai Bournou

AU - Koutsoukou, Maria

AU - Panagiotopoulou, Antigoni

AU - Voutos, Yorghos

AU - Bratsolis, Emmanuel

AU - Mylonas, Phivos

AU - Likforman-Sulem, Laurence

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Nowadays, governmental programs like ESA's Copernicus provide freely available data that can be easily utilized for earth observation. In the present work, the problem of detecting agricultural and non-agricultural land cover is addressed. The methodology is based on classification with convolutional neural networks (CNNs) and transfer learning using AlexNet. The study area is located at the Ionian Islands, which include several land cover classes according to Copernicus CORINE Land Cover 2018 (CLC 2018). Furthermore, the dataset consists of natural color images acquired by Sentinel-2A multi-spectral instrument. Experimentation proves that extra addition of training data from foreign grounds, unfamiliar to the Greek data, serves much as a confusing agent regarding network performance.

AB - Nowadays, governmental programs like ESA's Copernicus provide freely available data that can be easily utilized for earth observation. In the present work, the problem of detecting agricultural and non-agricultural land cover is addressed. The methodology is based on classification with convolutional neural networks (CNNs) and transfer learning using AlexNet. The study area is located at the Ionian Islands, which include several land cover classes according to Copernicus CORINE Land Cover 2018 (CLC 2018). Furthermore, the dataset consists of natural color images acquired by Sentinel-2A multi-spectral instrument. Experimentation proves that extra addition of training data from foreign grounds, unfamiliar to the Greek data, serves much as a confusing agent regarding network performance.

KW - Agricultural land

KW - CORINE database

KW - Deep Convolutional Neural Networks

KW - Deep learning

KW - Ionian Sea

KW - Remote Sensing

KW - Satellite Image Classification

KW - Transfer learning

U2 - 10.1109/IISA.2019.8900670

DO - 10.1109/IISA.2019.8900670

M3 - Conference contribution

AN - SCOPUS:85075883615

T3 - 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019

BT - 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019

Y2 - 15 July 2019 through 17 July 2019

ER -

Charou E, Felekis G, Stavroulopoulou DB, Koutsoukou M, Panagiotopoulou A, Voutos Y et al. Deep Learning for Agricultural Land Detection in Insular Areas. In 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8900670. (10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019). doi: 10.1109/IISA.2019.8900670

Deep Learning for Agricultural Land Detection in Insular Areas

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Keywords

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