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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

Yıl 2024, Cilt: 9 Sayı: 1, 12 - 24, 15.02.2024

Özlem Akar Ekrem Saralıoğlu Oğuz Güngör Halim Ferit Bayata

https://doi.org/10.26833/ijeg.1252298

Öz

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.

Anahtar Kelimeler

Machine Learning Deep Learning Random Forest Gabor Image Classification

Destekleyen Kurum

Erzincan Binali Yıldırım University Scientific Research Project

Proje Numarası

636

Teşekkür

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

Kaynakça

  • Weaver, R. J. (1976). Grape growing. John Wiley & Sons.
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  • 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.
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Yıl 2024, Cilt: 9 Sayı: 1, 12 - 24, 15.02.2024

Özlem Akar Ekrem Saralıoğlu Oğuz Güngör Halim Ferit Bayata

https://doi.org/10.26833/ijeg.1252298

Öz

Proje Numarası

636

Kaynakça

  • Weaver, R. J. (1976). Grape growing. John Wiley & Sons.
  • 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.
  • Republic of Turkey Ministry of Agriculture and Forestry. (2021). 2021-January Agricultural Products Markets Report: GRAPE, https://arastirma.tarimorman.gov.tr/tepge/Menu/27/Tarim-Urunleri-Piyasalari
  • Erzincan Directorate of Provincial Agriculture and Forestry (2022). https://erzincan.tarimorman.gov.tr/Menu/66/Tarimsal-Veriler
  • Christian, B., & Krishnayya, N. S. R. (2009). Classification of tropical trees growing in a sanctuary using Hyperion (EO-1) and SAM algorithm. Current Science, 96(12), 1601-1607.
  • 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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  • Abdullahi, H. S., Sheriff, R., & Mahieddine, F. (2017). Convolution neural network in precision agriculture for plant image recognition and classification. Seventh International Conference on Innovative Computing Technology (INTECH), 10, 256-272.
  • Zhao, H., Duan, S., Liu, J., Sun, L., & Reymondin, L. (2021). Evaluation of five deep learning models for crop type mapping using sentinel-2 time series images with missing information. Remote Sensing, 13(14), 2790. https://doi.org/10.3390/rs13142790
  • Zhong, L., Hu, L., & Zhou, H. (2019). Deep learning based multi-temporal crop classification. Remote Sensing of Environment, 221, 430-443. https://doi.org/10.1016/j.rse.2018.11.032
  • TR Erzincan Governorate. (2021). http://www.erzincan.gov.tr/erzincan-uzumu
  • Padwick, C., Deskevich, M., Pacifici, F., & Smallwood, S. (2010). WorldView-2 pan-sharpening. In Proceedings of the ASPRS 2010 Annual Conference, San Diego, CA, USA, 2630, 1-14.
  • Akar, Ö. (2019). Göktürk-2 ve Worldview-2 Uydu Görüntüleri için Görüntü Keskinleştirme Yöntemlerinin Değerlendirilmesi. Erzincan University Journal of Science and Technology, 12(2), 874-885.
  • Li, H., Jing, L., & Tang, Y. (2017). Assessment of pansharpening methods applied to WorldView-2 imagery fusion. Sensors, 17(1), 89. https://doi.org/10.3390/s17010089
  • Anshu, S. K., Pande, H., Tiwari, P. S., & Shukla, S. (2017). Evaluation of Fusion Techniques for High Resolution Data-A Worldview-2 Imagery. International Journal of Applied Remote Sensing and GIS, 4, 10-22.
  • Fu, L., Ma, J., Chen, Y., Larsson, R., & Zhao, J. (2019). Automatic detection of lung nodules using 3D deep convolutional neural networks. Journal of Shanghai Jiaotong University (Science), 24, 517-523. https://doi.org/10.1007/s12204-019-2084-4
  • Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. Geoscience and Remote Sensing Magazine, 5(4), 8-36. https://doi.org/10.1109/MGRS.2017.2762307
  • Ji, S., Xu, W., Yang, M., & Yu, K. (2012). 3D convolutional neural networks for human action recognition. Transactions on Pattern Analysis and Machine Intelligence, 35(1), 221-231. https://doi.org/10.1109/TPAMI.2012.59
  • Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015). Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision, 4489-4497.
  • Xu, Z., Guan, K., Casler, N., Peng, B., & Wang, S. (2018). A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery. ISPRS journal of photogrammetry and remote sensing, 144, 423-434. https://doi.org/10.1016/j.isprsjprs.2018.08.005
  • Ji, S., Zhang, C., Xu, A., Shi, Y., & Duan, Y. (2018). 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sensing, 10(1), 75. https://doi.org/10.3390/rs10010075
  • Mei, S., Yuan, X., Ji, J., Zhang, Y., Wan, S., & Du, Q. (2017). Hyperspectral image spatial super-resolution via 3D full convolutional neural network. Remote Sensing, 9(11), 1139. https://doi.org/10.3390/rs9111139
  • Saralioglu, E., & Gungor, O. (2022). Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network. Geocarto International, 37(2), 657-677. https://doi.org/10.1080/10106049.2020.1734871
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  • Stephens, D., & Diesing, M. (2014). A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data. PloS one, 9(4), e93950. https://doi.org/10.1371/journal.pone.0093950
  • Çölkesen, İ., & Yomralıoğlu, T. (2014). Arazi örtüsü ve kullanımının haritalanmasında WorldView-2 uydu görüntüsü ve yardımcı verilerin kullanımı. Harita Dergisi, 152(2), 12-24.
  • Thanh Noi, P., & Kappas, M. (2018). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors, 18(1), 18. https://doi.org/10.3390/s18010018
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  • Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices. CRC Press.
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Toplam 72 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Jeomatik Mühendisliği (Diğer)
Bölüm Research Article
Yazarlar

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

Proje Numarası 636
Erken Görünüm Tarihi 2 Ocak 2024
Yayımlanma Tarihi 15 Şubat 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 9 Sayı: 1

Kaynak Göster

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. Şubat 2024;9(1):12-24. doi:10.26833/ijeg.1252298
Chicago Akar, Özlem, Ekrem Saralıoğlu, Oğuz Güngör, ve 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, sy. 1 (Şubat 2024): 12-24. https://doi.org/10.26833/ijeg.1252298.
EndNote Akar Ö, Saralıoğlu E, Güngör O, Bayata HF (01 Şubat 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, ve 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, c. 9, sy. 1, ss. 12–24, 2024, doi: 10.26833/ijeg.1252298.
ISNAD Akar, Özlem vd. “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 (Şubat 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 vd. “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, c. 9, sy. 1, 2024, ss. 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.