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

Year 2024, Volume: 9 Issue: 1, 12 - 24, 15.02.2024
https://doi.org/10.26833/ijeg.1252298

Abstract

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 McNemar's 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.

Supporting Institution

Erzincan Binali Yıldırım University Scientific Research Project

Project Number

636

Thanks

This work was supported by Erzincan Binali Yıldırım University Scientific Research Project [Grant Number: 636].

References

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Year 2024, Volume: 9 Issue: 1, 12 - 24, 15.02.2024
https://doi.org/10.26833/ijeg.1252298

Abstract

Project Number

636

References

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  • Akpınar, E., & Çelikoğlu, Ş. (2016). Karaerik (Cimin) üzümünün Erzincan ekonomisine ve tanıtımına katkıları. Uluslararası Erzincan Sempozyumu, 2, 15-23.
  • Bulut, İ. (2006). Genel tarım bilgileri ve tarımın coğrafi esasları (Ziraat Coğrafyası). Gündüz Eğitim ve Yayıncılık, Ankara, 255.
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  • Erzincan Directorate of Provincial Agriculture and Forestry (2022). https://erzincan.tarimorman.gov.tr/Menu/66/Tarimsal-Veriler
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  • Prins, A. J., & Van Niekerk, A. (2020). Regional Mapping of Vineyards Using Machine Learning and LiDAR Data. International Journal of Applied Geospatial Research (IJAGR), 11(4), 1-22. https://doi.org/10.4018/IJAGR.2020100101
  • Darra, N., Psomiadis, E., Kasimati, A., Anastasiou, A., Anastasiou, E., & Fountas, S. (2021). Remote and proximal sensing-derived spectral indices and biophysical variables for spatial variation determination in vineyards. Agronomy, 11(4), 741. https://doi.org/10.3390/agronomy11040741
  • Vélez, S., Ariza-Sentís, M., & Valente, J. (2023). Mapping the spatial variability of Botrytis bunch rot risk in vineyards using UAV multispectral imagery. European Journal of Agronomy, 142, 126691. https://doi.org/10.1016/j.eja.2022.126691
  • Gungor, O., Boz, Y., Gokalp, E., Comert, C., & Akar, A. (2010). Fusion of low and high resolution satellite images to monitor changes on costal zones. Scientific Research and Essays, 5(7), 654-662.
  • Chi, M. V., Thi, L. P., & Si, S. T. (2009, October). Monitoring urban space expansion using Remote sensing data in Ha Long city, Quang Ninh province in Vietnam. In 7th FIG Regional Conference Spatial Data Serving People: Land Governance and the Environment–Building the Capacity Hanoi, Vietnam, 19-22.
  • Kaya, Y., & Polat, N. (2023). A linear approach for wheat yield prediction by using different spectral vegetation indices. International Journal of Engineering and Geosciences, 8(1), 52-62. https://doi.org/10.26833/ijeg.1035037
  • Akar, A., & Gökalp, E. (2018). Designing a sustainable rangeland information system for Turkey. International Journal of Engineering and Geosciences, 3(3), 87-97. https://doi.org/10.26833/ijeg.412222
  • Zhang, W., Xue, X., Sun, Z., Guo, Y. F., Chi, M., & Lu, H. (2007). Efficient feature extraction for image classification. IEEE 11th International Conference on Computer Vision, 1-8. https://doi.org/10.1109/ICCV.2007.4409058
  • Huang, Y., Fipps, G., Lacey, R. E., & Thomson, S. J. (2011). Landsat satellite multi-spectral image classification of land cover and land use changes for GIS-based urbanization analysis in irrigation districts of Lower Rio Grande Valley of Texas. Journal of Applied Remote Sensing, 2(1), 27-36.
  • Akar, Ö., & Tunç Görmüş, E. (2019). Göktürk-2 ve Hyperion EO-1 uydu görüntülerinden rastgele orman sınıflandırıcısı ve destek vektör makineleri ile arazi kullanım haritalarının üretilmesi. Geomatik, 4(1), 68-81. https://doi.org/10.29128/geomatik.476668
  • Ahady, A. B., & Kaplan, G. (2022). Classification comparison of Landsat-8 and Sentinel-2 data in Google Earth Engine, study case of the city of Kabul. International Journal of Engineering and Geosciences, 7(1), 24-31. https://doi.org/10.26833/ijeg.860077
  • Sefercik, U. G., Kavzoğlu, T., Çölkesen, I., Nazar, M., Öztürk, M. Y., Adali, S., & Dinç, S. (2023). 3D positioning accuracy and land cover classification performance of multispectral RTK UAVs. International Journal of Engineering and Geosciences, 8(2), 119-128. https://doi.org/10.26833/ijeg.1074791
  • Cengiz, A. V. C. I., Budak, M., Yağmur, N., & Balçik, F. (2023). Comparison between random forest and support vector machine algorithms for LULC classification. International Journal of Engineering and Geosciences, 8(1), 1-10. https://doi.org/10.26833/ijeg.987605
  • Tirmanoğlu, B., Ismailoğlu, I., Kokal, A. T., & Musaoğlu, N. (2023). Yeni nesil multispektral ve hiperspektral uydu görüntülerinin arazi örtüsü/arazi kullanımı sınıflandırma performanslarının karşılaştırılması: Sentinel-2 ve PRISMA Uydusu. Geomatik, 8(1), 79-90. https://doi.org/10.29128/geomatik.1126685
  • Çömert, R., Matci, D. K., & Avdan, U. (2019). Object based burned area mapping with random forest algorithm. International Journal of Engineering and Geosciences, 4(2), 78-87. https://doi.org/10.26833/ijeg.455595
  • Sun, Z., Di, L., Fang, H., & Burgess, A. (2020). Deep learning classification for crop types in north dakota. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2200-2213. https://doi.org/10.1109/JSTARS.2020.2990104
  • Gao, J. (2009). Digital analysis of remotely sensed imagery. McGraw-Hill Education, New York. ISBN: 9780071604659
  • Jay, S., Lawrence, R., Repasky, K., & Keith, C. (2009). Invasive species mapping using low-cost hyperspectral imagery. In ASPRS Annual Conference.
  • Ok, A. O., Akar, O., & Gungor, O. (2012). Evaluation of random forest method for agricultural crop classification. European Journal of Remote Sensing, 45(1), 421-432. https://doi.org/10.5721/EuJRS20124535
  • Akar, Ö., & Güngör, O. (2015). Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey. International Journal of Remote Sensing, 36(2), 442-464. https://doi.org/10.1080/01431161.2014.995276
  • Ntouros, K. D., Gitas, I. Z., & Silleos, G. N. (2009, August). Mapping agricultural crops with EO-1 Hyperion data. In 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 1-4. https://doi.org/10.1109/WHISPERS.2009.5289057
  • Kpienbaareh, D., Sun, X., Wang, J., Luginaah, I., Bezner Kerr, R., Lupafya, E., & Dakishoni, L. (2021). Crop type and land cover mapping in northern Malawi using the integration of sentinel-1, sentinel-2, and planetscope satellite data. Remote Sensing, 13(4), 700. https://doi.org/10.3390/rs13040700
  • Wang, S., Azzari, G., & Lobell, D. B. (2019). Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques. Remote sensing of environment, 222, 303-317. https://doi.org/10.1016/j.rse.2018.12.026
  • Akar, Ö., Saralıoğlu, E., Güngör, O., & Bayata, H. F. (2021). Determination of vineyards with support vector machine and deep learning-based Image classification. Intercontinental Geoinformation Days, 3, 26-29.
  • Grinblat, G. L., Uzal, L. C., Larese, M. G., & Granitto, P. M. (2016). Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture, 127, 418-424. https://doi.org/10.1016/j.compag.2016.07.003
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There are 72 citations in total.

Details

Primary Language English
Subjects Geomatic Engineering (Other)
Journal Section Research Article
Authors

Özlem Akar 0000-0001-6381-4907

Ekrem Saralıoğlu 0000-0002-0609-3338

Oğuz Güngör 0000-0002-3280-5466

Halim Ferit Bayata 0000-0001-8274-8888

Project Number 636
Early Pub Date January 2, 2024
Publication Date February 15, 2024
Published in Issue Year 2024 Volume: 9 Issue: 1

Cite

APA Akar, Ö., Saralıoğlu, E., Güngör, O., Bayata, H. F. (2024). Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection. International Journal of Engineering and Geosciences, 9(1), 12-24. https://doi.org/10.26833/ijeg.1252298
AMA Akar Ö, Saralıoğlu E, Güngör O, Bayata HF. Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection. IJEG. February 2024;9(1):12-24. doi:10.26833/ijeg.1252298
Chicago Akar, Özlem, Ekrem Saralıoğlu, Oğuz Güngör, and Halim Ferit Bayata. “Semantic Segmentation of Very-High Spatial Resolution Satellite Images: A Comparative Analysis of 3D-CNN and Traditional Machine Learning Algorithms for Automatic Vineyard Detection”. International Journal of Engineering and Geosciences 9, no. 1 (February 2024): 12-24. https://doi.org/10.26833/ijeg.1252298.
EndNote Akar Ö, Saralıoğlu E, Güngör O, Bayata HF (February 1, 2024) Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection. International Journal of Engineering and Geosciences 9 1 12–24.
IEEE Ö. Akar, E. Saralıoğlu, O. Güngör, and H. F. Bayata, “Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection”, IJEG, vol. 9, no. 1, pp. 12–24, 2024, doi: 10.26833/ijeg.1252298.
ISNAD Akar, Özlem et al. “Semantic Segmentation of Very-High Spatial Resolution Satellite Images: A Comparative Analysis of 3D-CNN and Traditional Machine Learning Algorithms for Automatic Vineyard Detection”. International Journal of Engineering and Geosciences 9/1 (February 2024), 12-24. https://doi.org/10.26833/ijeg.1252298.
JAMA Akar Ö, Saralıoğlu E, Güngör O, Bayata HF. Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection. IJEG. 2024;9:12–24.
MLA Akar, Özlem et al. “Semantic Segmentation of Very-High Spatial Resolution Satellite Images: A Comparative Analysis of 3D-CNN and Traditional Machine Learning Algorithms for Automatic Vineyard Detection”. International Journal of Engineering and Geosciences, vol. 9, no. 1, 2024, pp. 12-24, doi:10.26833/ijeg.1252298.
Vancouver Akar Ö, Saralıoğlu E, Güngör O, Bayata HF. Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection. IJEG. 2024;9(1):12-24.