Crop Classification with Attention Based BI-LSTM and Temporal Convolution Neural Network Combination for Remote Sensing Breizhcrop Time Series Data

Amer Bandar; Ahmet Coşkunçay

Makale detayları

Crop Classification with Attention Based BI-LSTM and Temporal Convolution Neural Network Combination for Remote Sensing Breizhcrop Time Series Data

Yazarlar:

Amer Bandar | Ahmet Coşkunçay

Yayıncı:

Van Yuzuncu Yıl University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.29 Issue 1, pp.173-188

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001316443

pISSN:

1300-5413

Anahtar kelimeler:

Özet:

 In the modern era, remote sensing data has become increasingly useful for determining land use and coverage requirements. Remote sensing data can be used for a variety of purposes, including the classification of crops. It is possible to aggregate remote sensing data for a specific area over time in order to obtain a more complete picture based on the time series of this data. One example of these types of data is the Breizhcrop dataset, which was collected using satellite images acquired by Sentinel 2 over a period of time. This study aims to investigate a neural network based on attention mechanisms using the BI-LSTM layer in conjunction with Temporal-CNN for the classification of crops. The aim of the research is to find a model for corps classification in image-based time series. In line with this goal, in addition to finding features over time, the presented model also needs to produce high-accuracy features at each time step to increase classification. Utilizing the designed neural network, we seek to find local features with the attention mechanism and general features with a second layer. This neural network was validated on the BreizhCrop dataset and we conclude that it performs better than alternative approaches. The proposed method has been compared with Temporal CNN, Star RNN, and Vanilla LSTM networks and it has obtained better results than the mentioned neural networks. Taking advantage of these local and global features that extract with developed model obtained a high accuracy rate of 82.

 In the modern era, remote sensing data has become increasingly useful for determining land use and coverage requirements. Remote sensing data can be used for a variety of purposes, including the classification of crops. It is possible to aggregate remote sensing data for a specific area over time in order to obtain a more complete picture based on the time series of this data. One example of these types of data is the Breizhcrop dataset, which was collected using satellite images acquired by Sentinel 2 over a period of time. This study aims to investigate a neural network based on attention mechanisms using the BI-LSTM layer in conjunction with Temporal-CNN for the classification of crops. The aim of the research is to find a model for corps classification in image-based time series. In line with this goal, in addition to finding features over time, the presented model also needs to produce high-accuracy features at each time step to increase classification. Utilizing the designed neural network, we seek to find local features with the attention mechanism and general features with a second layer. This neural network was validated on the BreizhCrop dataset and we conclude that it performs better than alternative approaches. The proposed method has been compared with Temporal CNN, Star RNN, and Vanilla LSTM networks and it has obtained better results than the mentioned neural networks. Taking advantage of these local and global features that extract with developed model obtained a high accuracy rate of 82.

 In the modern era, remote sensing data has become increasingly useful for determining land use and coverage requirements. Remote sensing data can be used for a variety of purposes, including the classification of crops. It is possible to aggregate remote sensing data for a specific area over time in order to obtain a more complete picture based on the time series of this data. One example of these types of data is the Breizhcrop dataset, which was collected using satellite images acquired by Sentinel 2 over a period of time. This study aims to investigate a neural network based on attention mechanisms using the BI-LSTM layer in conjunction with Temporal-CNN for the classification of crops. The aim of the research is to find a model for corps classification in image-based time series. In line with this goal, in addition to finding features over time, the presented model also needs to produce high-accuracy features at each time step to increase classification. Utilizing the designed neural network, we seek to find local features with the attention mechanism and general features with a second layer. This neural network was validated on the BreizhCrop dataset and we conclude that it performs better than alternative approaches. The proposed method has been compared with Temporal CNN, Star RNN, and Vanilla LSTM networks and it has obtained better results than the mentioned neural networks. Taking advantage of these local and global features that extract with developed model obtained a high accuracy rate of 82.

 Modern çağda, uzaktan algılama verileri, arazi kullanımı ve kaplama gereksinimlerini belirlemede giderek daha fazla kullanışlı hale gelmiştir. Uzaktan algılama verileri, aralarında mahsul sınıflandırması da bulunan çeşitli amaçlar için kullanılabilir. Belirli bir alan için uzaktan algılama verilerini zaman içinde toplamak, bu verilerin zaman serisi temelinde daha kapsamlı bir görüntü elde etmeyi mümkün kılar. Bu tür verilere örnek olarak, bir süre boyunca Sentinel 2 tarafından elde edilen uydu görüntüleri kullanılarak toplanan Breizhcrop veri seti gösterilebilir. Bu çalışma, mahsullerin sınıflandırılması için BI-LSTM katmanı ile Zaman-İlişkili CNNnin birleşiminde dayanan, dikkat mekanizmaları temelinde bir sinir ağı araştırmayı hedeflemektedir. Araştırmanın amacı, görüntü tabanlı zaman serilerinde mahsul sınıflandırması için bir model bulmaktır. Bu hedef doğrultusunda, zaman içinde özellikler bulmanın yanı sıra, sunulan modelin her zaman adımında yüksek doğrulukta özellikler üretmesi gerekmektedir ki bu da sınıflandırmayı artırsın. Tasarlanan sinir ağı ile yerel özellikleri dikkat mekanizması ile ve genel özellikleri ikinci bir katman ile bulmayı amaçlıyoruz. Bu sinir ağı, BreizhCrop veri seti üzerinde doğrulanmış ve alternatif yaklaşımlara göre daha iyi performans sergilediği sonucuna varılmıştır. Önerilen yöntem, Zaman-İlişkili CNN, Star RNN ve Vanilya LSTM ağları ile karşılaştırılmış ve bahsedilen sinir ağlarından daha iyi sonuçlar elde edilmiştir. Geliştirilen modelle çıkarılan bu yerel ve küresel özelliklerin avantajını kullanarak, 82 gibi yüksek bir doğruluk oranı elde edilmiştir.

Makale detayları

Crop Classification with Attention Based BI-LSTM and Temporal Convolution Neural Network Combination for Remote Sensing Breizhcrop Time Series Data

Yazarlar:

Amer Bandar | Ahmet Coşkunçay

Yayıncı:

Van Yuzuncu Yıl University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.29 Issue 1, pp.173-188

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001316443

pISSN:

1300-5413

Attention mechanism

Crop classification

Land use and coverage

Remote sensing

Time seriesArazi kullanımı ve kapsamı

Dikkat mekanizması

Mahsul sınıflandırması

Uzaktan algılama

Zaman serisi

Sayı

Makale detayları

Yazarlar: Amer Bandar | Ahmet Coşkunçay

Yayıncı: Van Yuzuncu Yıl University

Ana Başlıklar: Bilgi Sistemleri (Diğer)

Dergi: Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.29 Issue 1, pp.173-188

Belge Tipi: Makale

Yayın Tarihi: 4/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001316443

pISSN: 1300-5413

Sayı

Yazarlar:

Amer Bandar | Ahmet Coşkunçay

Yayıncı:

Van Yuzuncu Yıl University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.29 Issue 1, pp.173-188

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001316443

pISSN:

1300-5413