Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection

Özlem Akar; Ekrem Saralıoğlu; Oğuz Güngör; Halim Ferit Bayata

Makale detayları

Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection

Yazarlar:

Özlem Akar | Ekrem Saralıoğlu | Oğuz Güngör | Halim Ferit Bayata

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Geomatic Engineering (Other)

Dergi:

International Journal of Engineering and Geosciences Vol.9 Issue 1, pp.12-24

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309433

pISSN:

2548-0960

Anahtar kelimeler:

Özet:

 The Erzincan Cimin grape, which is an endemic product, plays a significant role in the economy of both the region it is cultivated in and the overall country. Therefore, it is crucial to closely monitor and promote this product. The objective of this study was to analyze the spatial distribution of vineyards by utilizing advanced machine learning and deep learning algorithms to classify high-resolution satellite images. A deep learning model based on a 3D Convolutional Neural Network CNN was developed for vineyard classification. The proposed model was compared with traditional machine learning algorithms, specifically Support Vector Machine SVM, Random Forest RF, and Rotation Forest ROTF. The accuracy of the classifications was assessed through error matrices, kappa analysis, and McNemar tests. The best overall classification accuracies and kappa values were achieved by the 3D CNN and RF methods, with scores of 86.47 0.8308 and 70.53 0.6279 respectively. Notably, when Gabor texture features were incorporated, the accuracy of the RF method increased to 75.94 0.6364. Nevertheless, the 3D CNN classifier outperformed all others, yielding the highest classification accuracy with an 11 advantage 86.47. The statistical analysis using McNemars test confirmed that the χ2 values for all classification outcomes exceeded 3.84 at the 95 confidence interval, indicating a significant enhancement in classification accuracy provided by the 3D CNN classifier. Additionally, the 3D CNN method demonstrated successful classification performance, as evidenced by the minimum-maximum F1-score 0.79-0.97, specificity 0.95-0.99, and accuracy 0.91-0.99 values.

 The Erzincan Cimin grape, which is an endemic product, plays a significant role in the economy of both the region it is cultivated in and the overall country. Therefore, it is crucial to closely monitor and promote this product. The objective of this study was to analyze the spatial distribution of vineyards by utilizing advanced machine learning and deep learning algorithms to classify high-resolution satellite images. A deep learning model based on a 3D Convolutional Neural Network CNN was developed for vineyard classification. The proposed model was compared with traditional machine learning algorithms, specifically Support Vector Machine SVM, Random Forest RF, and Rotation Forest ROTF. The accuracy of the classifications was assessed through error matrices, kappa analysis, and McNemar tests. The best overall classification accuracies and kappa values were achieved by the 3D CNN and RF methods, with scores of 86.47 0.8308 and 70.53 0.6279 respectively. Notably, when Gabor texture features were incorporated, the accuracy of the RF method increased to 75.94 0.6364. Nevertheless, the 3D CNN classifier outperformed all others, yielding the highest classification accuracy with an 11 advantage 86.47. The statistical analysis using McNemars test confirmed that the χ2 values for all classification outcomes exceeded 3.84 at the 95 confidence interval, indicating a significant enhancement in classification accuracy provided by the 3D CNN classifier. Additionally, the 3D CNN method demonstrated successful classification performance, as evidenced by the minimum-maximum F1-score 0.79-0.97, specificity 0.95-0.99, and accuracy 0.91-0.99 values.

 The Erzincan Cimin grape, which is an endemic product, plays a significant role in the economy of both the region it is cultivated in and the overall country. Therefore, it is crucial to closely monitor and promote this product. The objective of this study was to analyze the spatial distribution of vineyards by utilizing advanced machine learning and deep learning algorithms to classify high-resolution satellite images. A deep learning model based on a 3D Convolutional Neural Network CNN was developed for vineyard classification. The proposed model was compared with traditional machine learning algorithms, specifically Support Vector Machine SVM, Random Forest RF, and Rotation Forest ROTF. The accuracy of the classifications was assessed through error matrices, kappa analysis, and McNemar tests. The best overall classification accuracies and kappa values were achieved by the 3D CNN and RF methods, with scores of 86.47 0.8308 and 70.53 0.6279 respectively. Notably, when Gabor texture features were incorporated, the accuracy of the RF method increased to 75.94 0.6364. Nevertheless, the 3D CNN classifier outperformed all others, yielding the highest classification accuracy with an 11 advantage 86.47. The statistical analysis using McNemars test confirmed that the χ2 values for all classification outcomes exceeded 3.84 at the 95 confidence interval, indicating a significant enhancement in classification accuracy provided by the 3D CNN classifier. Additionally, the 3D CNN method demonstrated successful classification performance, as evidenced by the minimum-maximum F1-score 0.79-0.97, specificity 0.95-0.99, and accuracy 0.91-0.99 values.

 The Erzincan Cimin grape, which is an endemic product, plays a significant role in the economy of both the region it is cultivated in and the overall country. Therefore, it is crucial to closely monitor and promote this product. The objective of this study was to analyze the spatial distribution of vineyards by utilizing advanced machine learning and deep learning algorithms to classify high-resolution satellite images. A deep learning model based on a 3D Convolutional Neural Network CNN was developed for vineyard classification. The proposed model was compared with traditional machine learning algorithms, specifically Support Vector Machine SVM, Random Forest RF, and Rotation Forest ROTF. The accuracy of the classifications was assessed through error matrices, kappa analysis, and McNemar tests. The best overall classification accuracies and kappa values were achieved by the 3D CNN and RF methods, with scores of 86.47 0.8308 and 70.53 0.6279 respectively. Notably, when Gabor texture features were incorporated, the accuracy of the RF method increased to 75.94 0.6364. Nevertheless, the 3D CNN classifier outperformed all others, yielding the highest classification accuracy with an 11 advantage 86.47. The statistical analysis using McNemars test confirmed that the χ2 values for all classification outcomes exceeded 3.84 at the 95 confidence interval, indicating a significant enhancement in classification accuracy provided by the 3D CNN classifier. Additionally, the 3D CNN method demonstrated successful classification performance, as evidenced by the minimum-maximum F1-score 0.79-0.97, specificity 0.95-0.99, and accuracy 0.91-0.99 values.

Makale detayları

Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection

Yazarlar:

Özlem Akar | Ekrem Saralıoğlu | Oğuz Güngör | Halim Ferit Bayata

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Geomatic Engineering (Other)

Dergi:

International Journal of Engineering and Geosciences Vol.9 Issue 1, pp.12-24

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309433

pISSN:

2548-0960

Machine Learning

Deep Learning

Random Forest

Gabor

Image Classification

Sayı

Makale detayları

Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection

Yazarlar: Özlem Akar | Ekrem Saralıoğlu | Oğuz Güngör | Halim Ferit Bayata

Yayıncı: Murat YAKAR

Ana Başlıklar: Geomatic Engineering (Other)

Dergi: International Journal of Engineering and Geosciences Vol.9 Issue 1, pp.12-24

Belge Tipi: Makale

Yayın Tarihi: 2/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001309433

pISSN: 2548-0960

Machine Learning

Deep Learning

Random Forest

Gabor

Image Classification

Sayı

Yazarlar:

Özlem Akar | Ekrem Saralıoğlu | Oğuz Güngör | Halim Ferit Bayata

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Geomatic Engineering (Other)

Dergi:

International Journal of Engineering and Geosciences Vol.9 Issue 1, pp.12-24

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309433

pISSN:

2548-0960