Research Article
BibTex RIS Cite

Optik ve radar görüntüleri ile aşırı gradyan artırma algoritması kullanılarak tarımsal ürün desen tespiti

Year 2024, Volume: 9 Issue: 1, 54 - 68, 15.04.2024

Fatih Fehmi Şimşek

https://doi.org/10.29128/geomatik.1332997

Abstract

Bu çalışmada, Sentinel-1 Sentetik Açıklıklı Radar (Synthetic Aperture Radar-SAR) ve Sentinel-2 (Multispektral) verilerinin, sınıflandırma ile tarımsal ürün deseni tespitine olan etkisi araştırılmıştır. Çalışma alanı Çukurova Ovası sınırları içerisinde bulunan yaklaşık 2200 km2’lik alanı kapsamaktadır. Çalışma kapsamında 2021 yılına ait çok zamanlı Sentinel-1 ve Sentinel-2 görüntüleri ile aşırı gradyan arttırma (XGBoost) algoritması kullanılarak mısır, pamuk, buğday, ayçiçeği, karpuz, yer fıstığı ve narenciye ağaçlarının yanı sıra, buğdaydan sonra ekilen ikinci ürün mısır, soya ve pamuk ürünlerini içeren tarımsal ürün desen sınıflandırması yapılmıştır. Çalışmada referans parsel olarak Çiftçi Kayıt Sistemi (ÇKS)’ne kayıtlı parseller kullanılmış olup, ÇKS verisinin yer doğruluk verisi olarak kullanılmasından önce ön düzenleme ve kural tabanlı silme işlemleri gerçekleştirilmiş, ardından hatalı ve yanlış beyanlar elemine edilmiştir. Çalışmada yalnızca Sentinel-1 verileri ile (VH, VV, VH/VV) yapılan sınıflandırma sonucu genel doğruluk değeri %72.3, yalnızca Sentinel-2 verileri ile (R, G, B, NIR, NDVI) yapılan sınıflandırma sonucu genel doğruluk değeri %87.2, Sentinel-1 ve Sentinel-2 verilerinin birlikte kullanıldığı sınıflandırma sonucunda ise genel doğruluk değeri %92.1 olarak hesaplanmıştır. Sınıflandırma çalışması ürün bazında incelendiğinde en düşük doğruluğu yine sadece Sentinel-1 verileri ile hesaplanan sınıflara ait iken, en yüksek doğruluk oranı Sentinel-1 ve Sentinel-2 verilerinin birlikte kullanıldığı sınıflandırmaya ait olduğu tespit edilmiştir. Özellikle çok yakın fenolojik dönemlere sahip olan ikinci ürünlerde Sentinel-1 ve Sentinel-2 verilerinin birlikte kullanılmasının, başarım oranını oldukça arttığı tespit edilmiştir.

Keywords

Uzaktan Algılama Sentinel-1 Sentinel-2 ÇKS XGBoost

References

  • Acar, E., & Altun, M. (2021). Classification of the agricultural crops using landsat-8 NDVI parameters by support vector machine. Balkan Journal of Electrical and Computer Engineering, 9(1), 78-82. https://doi.org/10.17694/bajece.863147
  • Altun, M., & Turker, M. (2022). Integration of Sentinel-1 and Landsat-8 images for crop detection: The case study of Manisa, Turkey. Advanced Remote Sensing, 2(1), 23-33.
  • Bağcı, R. Ş., Acar, E., & Türk, Ö. (2023). Identification of cotton and corn plant areas by employing deep transformer encoder approach and different time series satellite images: A case study in Diyarbakir, Turkey. Computers and Electronics in Agriculture, 209, 107838. https://doi.org/10.1016/j.compag.2023.107838
  • Bort Escabias, C. (2017). Tree Boosting Data Competitions with XGBoost [Master's thesis, Universitat Politècnica de Catalunya].
  • Cai, Y., Lin, H., & Zhang, M. (2019). Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data. Advances in Space Research, 64(11), 2233-2244. https://doi.org/10.1016/j.asr.2019.08.042
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining,785-794. https://doi.org/10.1145/2939672.2939785
  • Chen, X., Wang, Z. X., & Pan, X. M. (2019). HIV-1 tropism prediction by the XGboost and HMM methods. Scientific Reports, 9(1), 9997. https://doi.org/10.1038/s41598-019-46420-4
  • Çabuk, S. (2021). Aşırı Gradyan Artırma Algoritması kullanarak Sentınel-1 zaman serisi görüntülerinden ürün sınıflandırma. [Yüksek Lisans Tezi, Hacettepe Üniversitesi].
  • Dobrinić, D., Medak, D., & Gašparović, M. (2020). Integration of multitemporal Sentinel-1 and Sentinel-2 imagery for land-cover classification using machine learning methods. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 91-98. https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-91-2020
  • Duysak, H., & Yiğit, E. (2022). Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets. International Journal of Engineering and Geosciences, 7(1), 81-90. https://doi.org/10.26833/ijeg.882589
  • Efe, E., & Alganci, U. (2023). Çok zamanlı Sentinel 2 uydu görüntüleri ve makine öğrenmesi tabanlı algoritmalar ile arazi örtüsü değişiminin belirlenmesi. Geomatik, 8(1), 27-34. https://doi.org/10.29128/geomatik.1092838
  • Fan, J., Zhang, X., Zhao, C., Qin, Z., De Vroey, M., & Defourny, P. (2021). Evaluation of crop type classification with different high resolution satellite data sources. Remote Sensing, 13(5), 911. https://doi.org/10.3390/rs13050911
  • Farid, D. M., Maruf, G. M., & Rahman, C. M. (2013, May). A new approach of Boosting using decision tree classifier for classifying noisy data. In 2013 International Conference on Informatics, Electronics and Vision (ICIEV), 1-4. https://doi.org/10.1109/ICIEV.2013.6572718
  • Filipponi, F. (2019). Sentinel-1 GRD preprocessing workflow. In International Electronic Conference on Remote Sensing (p. 11). https://doi.org/10.3390/ECRS-3-06201
  • Fitriah, N., Wijaya, S. K., Fanany, M. I., Badri, C., & Rezal, M. (2017, July). EEG channels reduction using PCA to increase XGBoost’s accuracy for stroke detection. In AIP Conference Proceedings, 1862(1). https://doi.org/10.1063/1.4991232
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
  • Jiao, X., Kovacs, J. M., Shang, J., McNairn, H., Walters, D., Ma, B., & Geng, X. (2014). Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 38-46. https://doi.org/10.1016/j.isprsjprs.2014.06.014
  • Khabbazan, S., Vermunt, P., Steele-Dunne, S., Ratering Arntz, L., Marinetti, C., van der Valk, D., ... & van der Sande, C. (2019). Crop monitoring using Sentinel-1 data: A case study from The Netherlands. Remote Sensing, 11(16), 1887. https://doi.org/10.3390/rs11161887
  • Lee, J. S., Jurkevich, L., Dewaele, P., Wambacq, P., & Oosterlinck, A. (1994). Speckle filtering of synthetic aperture radar images: A review. Remote sensing reviews, 8(4), 313-340. https://doi.org/10.1080/02757259409532206
  • Lemoine, G., & Leo, O. (2015, July). Crop mapping applications at scale: Using Google Earth Engine to enable global crop area and status monitoring using free and open data sources. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1496-1499. https://doi.org/10.1109/IGARSS.2015.7326063
  • Lussem, U., Hütt, C., & Waldhoff, G. (2016). Combined analysis of Sentinel-1 and RapidEye data for improved crop type classification: An early season approach for rapeseed and cereals. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 959-963. https://doi.org/10.5194/isprsarchives-XLI-B8-959-2016
  • McNairn, H., & Shang, J. (2016). A review of multitemporal synthetic aperture radar (SAR) for crop monitoring. Multitemporal Remote Sensing: Methods and Applications, 317-340. https://doi.org/10.1007/978-3-319-47037-5_15
  • Mercier, A., Betbeder, J., Rapinel, S., Jegou, N., Baudry, J., & Hubert-Moy, L. (2020). Evaluation of Sentinel-1 and-2 time series for estimating LAI and biomass of wheat and rapeseed crop types. Journal of Applied Remote Sensing, 14(2), 024512. https://doi.org/10.1117/1.JRS.14.024512
  • Mitchell, R., & Frank, E. (2017). Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science, 3, e127. https://doi.org/10.7717/peerj-cs.127
  • Morsy, S., & Hadi, M. (2022). Impact of land use/land cover on land surface temperature and its relationship with spectral indices in Dakahlia Governorate, Egypt. International Journal of Engineering and Geosciences, 7(3), 272-282. https://doi.org/10.26833/ijeg.978961
  • Müller-Wilm, U., Devignot, O., & Pessiot, L. (2017). Sen2Cor Configuration and User Manual. S2-PDGS-MPC-L2A-SUM-V2. 4.
  • Nasirzadehdizaji, R., Sanli, F. B., Cakir, Z., & Sertel, E. (2019, July). Crop mapping improvement by combination of optical and SAR datasets. In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) 1-6. https://doi.org/10.1109/Agro-Geoinformatics.2019.8820604
  • Patrous, Z. S. (2018). Evaluating xgboost for user classification by using behavioral features extracted from smartphone sensors, [Master Thesis, KTH Royal Institute of Technology].
  • Polat, A. B., Sanli, F. B., & Akcay, O. (2022). Analyzing rice farming between sowing and harvest time with Sentinel-1 SAR data. Advanced Remote Sensing, 2(1), 34-39.
  • Saini, R., & Ghosh, S. K. (2021). Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery. Geocarto international, 36(19), 2141-2159. https://doi.org/10.1080/10106049.2019.1700556
  • Skakun, S., Kussul, N., Shelestov, A. Y., Lavreniuk, M., & Kussul, O. (2015). Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8), 3712-3719. https://doi.org/10.1109/JSTARS.2015.2454297
  • Small, D., & Schubert, A. (2008). Guide to ASAR geocoding. ESA-ESRIN Technical Note RSL-ASAR-GC-AD, 1, 36.
  • Sun, L., Chen, J., Guo, S., Deng, X., & Han, Y. (2020). Integration of time series sentinel-1 and sentinel-2 imagery for crop type mapping over oasis agricultural areas. Remote Sensing, 12(1), 158. https://doi.org/10.3390/rs12010158
  • Şimşek, F. F., & Durduran, S. S. (2023). Açık kaynak kodlu Eo-learn kütüphanesi ve çok zamanlı Sentinel-2 görüntüleri ile tarımsal ürün sınıflandırması. Jeodezi ve Jeoinformasyon Dergisi, 10(1), 45-62. https://doi.org/10.9733/JGG.2023R0004.T
  • Türk, S. T., & Balçık, F. (2023). Rastgele orman algoritması ve Sentinel-2 MSI ile fındık ekili alanların belirlenmesi: Piraziz Örneği. Geomatik, 8(2), 91-98. https://doi.org/10.29128/geomatik.1127925
  • URL1: https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1
  • URL2: https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2
  • Üstüner, M., Abdikan, S., Bilgin, G., & Şanlı, F. B. (2020). Hafif gradyan artırma makineleri ile tarımsal ürünlerin sınıflandırılması. Türk Uzaktan Algılama ve CBS Dergisi, 1(2), 97-105.
  • Viana, C. M., Girão, I., & Rocha, J. (2019). Long-term satellite image time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sensing, 11(9), 1104. https://doi.org/10.3390/rs11091104
  • Zhang, H., Kang, J., Xu, X., & Zhang, L. (2020). Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi’an County, Heilongjiang province, China. Computers and Electronics in Agriculture, 176, 105618. https://doi.org/10.1016/j.compag.2020.105618

Year 2024, Volume: 9 Issue: 1, 54 - 68, 15.04.2024

Fatih Fehmi Şimşek

https://doi.org/10.29128/geomatik.1332997

Abstract

References

  • Acar, E., & Altun, M. (2021). Classification of the agricultural crops using landsat-8 NDVI parameters by support vector machine. Balkan Journal of Electrical and Computer Engineering, 9(1), 78-82. https://doi.org/10.17694/bajece.863147
  • Altun, M., & Turker, M. (2022). Integration of Sentinel-1 and Landsat-8 images for crop detection: The case study of Manisa, Turkey. Advanced Remote Sensing, 2(1), 23-33.
  • Bağcı, R. Ş., Acar, E., & Türk, Ö. (2023). Identification of cotton and corn plant areas by employing deep transformer encoder approach and different time series satellite images: A case study in Diyarbakir, Turkey. Computers and Electronics in Agriculture, 209, 107838. https://doi.org/10.1016/j.compag.2023.107838
  • Bort Escabias, C. (2017). Tree Boosting Data Competitions with XGBoost [Master's thesis, Universitat Politècnica de Catalunya].
  • Cai, Y., Lin, H., & Zhang, M. (2019). Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data. Advances in Space Research, 64(11), 2233-2244. https://doi.org/10.1016/j.asr.2019.08.042
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining,785-794. https://doi.org/10.1145/2939672.2939785
  • Chen, X., Wang, Z. X., & Pan, X. M. (2019). HIV-1 tropism prediction by the XGboost and HMM methods. Scientific Reports, 9(1), 9997. https://doi.org/10.1038/s41598-019-46420-4
  • Çabuk, S. (2021). Aşırı Gradyan Artırma Algoritması kullanarak Sentınel-1 zaman serisi görüntülerinden ürün sınıflandırma. [Yüksek Lisans Tezi, Hacettepe Üniversitesi].
  • Dobrinić, D., Medak, D., & Gašparović, M. (2020). Integration of multitemporal Sentinel-1 and Sentinel-2 imagery for land-cover classification using machine learning methods. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 91-98. https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-91-2020
  • Duysak, H., & Yiğit, E. (2022). Investigation of the performance of different wavelet-based fusions of SAR and optical images using Sentinel-1 and Sentinel-2 datasets. International Journal of Engineering and Geosciences, 7(1), 81-90. https://doi.org/10.26833/ijeg.882589
  • Efe, E., & Alganci, U. (2023). Çok zamanlı Sentinel 2 uydu görüntüleri ve makine öğrenmesi tabanlı algoritmalar ile arazi örtüsü değişiminin belirlenmesi. Geomatik, 8(1), 27-34. https://doi.org/10.29128/geomatik.1092838
  • Fan, J., Zhang, X., Zhao, C., Qin, Z., De Vroey, M., & Defourny, P. (2021). Evaluation of crop type classification with different high resolution satellite data sources. Remote Sensing, 13(5), 911. https://doi.org/10.3390/rs13050911
  • Farid, D. M., Maruf, G. M., & Rahman, C. M. (2013, May). A new approach of Boosting using decision tree classifier for classifying noisy data. In 2013 International Conference on Informatics, Electronics and Vision (ICIEV), 1-4. https://doi.org/10.1109/ICIEV.2013.6572718
  • Filipponi, F. (2019). Sentinel-1 GRD preprocessing workflow. In International Electronic Conference on Remote Sensing (p. 11). https://doi.org/10.3390/ECRS-3-06201
  • Fitriah, N., Wijaya, S. K., Fanany, M. I., Badri, C., & Rezal, M. (2017, July). EEG channels reduction using PCA to increase XGBoost’s accuracy for stroke detection. In AIP Conference Proceedings, 1862(1). https://doi.org/10.1063/1.4991232
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
  • Jiao, X., Kovacs, J. M., Shang, J., McNairn, H., Walters, D., Ma, B., & Geng, X. (2014). Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 38-46. https://doi.org/10.1016/j.isprsjprs.2014.06.014
  • Khabbazan, S., Vermunt, P., Steele-Dunne, S., Ratering Arntz, L., Marinetti, C., van der Valk, D., ... & van der Sande, C. (2019). Crop monitoring using Sentinel-1 data: A case study from The Netherlands. Remote Sensing, 11(16), 1887. https://doi.org/10.3390/rs11161887
  • Lee, J. S., Jurkevich, L., Dewaele, P., Wambacq, P., & Oosterlinck, A. (1994). Speckle filtering of synthetic aperture radar images: A review. Remote sensing reviews, 8(4), 313-340. https://doi.org/10.1080/02757259409532206
  • Lemoine, G., & Leo, O. (2015, July). Crop mapping applications at scale: Using Google Earth Engine to enable global crop area and status monitoring using free and open data sources. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1496-1499. https://doi.org/10.1109/IGARSS.2015.7326063
  • Lussem, U., Hütt, C., & Waldhoff, G. (2016). Combined analysis of Sentinel-1 and RapidEye data for improved crop type classification: An early season approach for rapeseed and cereals. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 959-963. https://doi.org/10.5194/isprsarchives-XLI-B8-959-2016
  • McNairn, H., & Shang, J. (2016). A review of multitemporal synthetic aperture radar (SAR) for crop monitoring. Multitemporal Remote Sensing: Methods and Applications, 317-340. https://doi.org/10.1007/978-3-319-47037-5_15
  • Mercier, A., Betbeder, J., Rapinel, S., Jegou, N., Baudry, J., & Hubert-Moy, L. (2020). Evaluation of Sentinel-1 and-2 time series for estimating LAI and biomass of wheat and rapeseed crop types. Journal of Applied Remote Sensing, 14(2), 024512. https://doi.org/10.1117/1.JRS.14.024512
  • Mitchell, R., & Frank, E. (2017). Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science, 3, e127. https://doi.org/10.7717/peerj-cs.127
  • Morsy, S., & Hadi, M. (2022). Impact of land use/land cover on land surface temperature and its relationship with spectral indices in Dakahlia Governorate, Egypt. International Journal of Engineering and Geosciences, 7(3), 272-282. https://doi.org/10.26833/ijeg.978961
  • Müller-Wilm, U., Devignot, O., & Pessiot, L. (2017). Sen2Cor Configuration and User Manual. S2-PDGS-MPC-L2A-SUM-V2. 4.
  • Nasirzadehdizaji, R., Sanli, F. B., Cakir, Z., & Sertel, E. (2019, July). Crop mapping improvement by combination of optical and SAR datasets. In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) 1-6. https://doi.org/10.1109/Agro-Geoinformatics.2019.8820604
  • Patrous, Z. S. (2018). Evaluating xgboost for user classification by using behavioral features extracted from smartphone sensors, [Master Thesis, KTH Royal Institute of Technology].
  • Polat, A. B., Sanli, F. B., & Akcay, O. (2022). Analyzing rice farming between sowing and harvest time with Sentinel-1 SAR data. Advanced Remote Sensing, 2(1), 34-39.
  • Saini, R., & Ghosh, S. K. (2021). Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery. Geocarto international, 36(19), 2141-2159. https://doi.org/10.1080/10106049.2019.1700556
  • Skakun, S., Kussul, N., Shelestov, A. Y., Lavreniuk, M., & Kussul, O. (2015). Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8), 3712-3719. https://doi.org/10.1109/JSTARS.2015.2454297
  • Small, D., & Schubert, A. (2008). Guide to ASAR geocoding. ESA-ESRIN Technical Note RSL-ASAR-GC-AD, 1, 36.
  • Sun, L., Chen, J., Guo, S., Deng, X., & Han, Y. (2020). Integration of time series sentinel-1 and sentinel-2 imagery for crop type mapping over oasis agricultural areas. Remote Sensing, 12(1), 158. https://doi.org/10.3390/rs12010158
  • Şimşek, F. F., & Durduran, S. S. (2023). Açık kaynak kodlu Eo-learn kütüphanesi ve çok zamanlı Sentinel-2 görüntüleri ile tarımsal ürün sınıflandırması. Jeodezi ve Jeoinformasyon Dergisi, 10(1), 45-62. https://doi.org/10.9733/JGG.2023R0004.T
  • Türk, S. T., & Balçık, F. (2023). Rastgele orman algoritması ve Sentinel-2 MSI ile fındık ekili alanların belirlenmesi: Piraziz Örneği. Geomatik, 8(2), 91-98. https://doi.org/10.29128/geomatik.1127925
  • URL1: https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1
  • URL2: https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2
  • Üstüner, M., Abdikan, S., Bilgin, G., & Şanlı, F. B. (2020). Hafif gradyan artırma makineleri ile tarımsal ürünlerin sınıflandırılması. Türk Uzaktan Algılama ve CBS Dergisi, 1(2), 97-105.
  • Viana, C. M., Girão, I., & Rocha, J. (2019). Long-term satellite image time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sensing, 11(9), 1104. https://doi.org/10.3390/rs11091104
  • Zhang, H., Kang, J., Xu, X., & Zhang, L. (2020). Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi’an County, Heilongjiang province, China. Computers and Electronics in Agriculture, 176, 105618. https://doi.org/10.1016/j.compag.2020.105618
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Photogrammetry and Remote Sensing
Journal Section Makaleler
Authors

Fatih Fehmi Şimşek 0000-0003-4016-4408

Early Pub Date February 5, 2024
Publication Date April 15, 2024
Published in Issue Year 2024 Volume: 9 Issue: 1

Cite

APA Şimşek, F. F. (2024). Optik ve radar görüntüleri ile aşırı gradyan artırma algoritması kullanılarak tarımsal ürün desen tespiti. Geomatik, 9(1), 54-68. https://doi.org/10.29128/geomatik.1332997
AMA Şimşek FF. Optik ve radar görüntüleri ile aşırı gradyan artırma algoritması kullanılarak tarımsal ürün desen tespiti. Geomatik. April 2024;9(1):54-68. doi:10.29128/geomatik.1332997
Chicago Şimşek, Fatih Fehmi. “Optik Ve Radar görüntüleri Ile aşırı Gradyan artırma Algoritması kullanılarak tarımsal ürün Desen Tespiti”. Geomatik 9, no. 1 (April 2024): 54-68. https://doi.org/10.29128/geomatik.1332997.
EndNote Şimşek FF (April 1, 2024) Optik ve radar görüntüleri ile aşırı gradyan artırma algoritması kullanılarak tarımsal ürün desen tespiti. Geomatik 9 1 54–68.
IEEE F. F. Şimşek, “Optik ve radar görüntüleri ile aşırı gradyan artırma algoritması kullanılarak tarımsal ürün desen tespiti”, Geomatik, vol. 9, no. 1, pp. 54–68, 2024, doi: 10.29128/geomatik.1332997.
ISNAD Şimşek, Fatih Fehmi. “Optik Ve Radar görüntüleri Ile aşırı Gradyan artırma Algoritması kullanılarak tarımsal ürün Desen Tespiti”. Geomatik 9/1 (April 2024), 54-68. https://doi.org/10.29128/geomatik.1332997.
JAMA Şimşek FF. Optik ve radar görüntüleri ile aşırı gradyan artırma algoritması kullanılarak tarımsal ürün desen tespiti. Geomatik. 2024;9:54–68.
MLA Şimşek, Fatih Fehmi. “Optik Ve Radar görüntüleri Ile aşırı Gradyan artırma Algoritması kullanılarak tarımsal ürün Desen Tespiti”. Geomatik, vol. 9, no. 1, 2024, pp. 54-68, doi:10.29128/geomatik.1332997.
Vancouver Şimşek FF. Optik ve radar görüntüleri ile aşırı gradyan artırma algoritması kullanılarak tarımsal ürün desen tespiti. Geomatik. 2024;9(1):54-68.