Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery

Esin Karamanlı; Ömer Faruk Uzun

doi:10.58816/duzceod.1675848

TR EN

Sentinel 2 MSI Uydu Görüntülerini Kullanarak Spektral İndeks Çıkarımına Dayalı Peyzaj Düzeyinde Nesne Tabanlı Görüntü Sınıflandırma Süreci

Öz

Arazi Örtüsü-Arazi Kullanımı (AÖ-AK) sınıflandırması, peyzaj ölçeğinde çevresel ve ekolojik kararların etkin şekilde yönetilmesi için veriler sunar. Bu süreçte, Sentinel-2 Multispektral Görüntüleyici (MSI) uydu görüntüleri, yüksek spektral çözünürlükleriyle bilgi çıkarımını kolaylaştırarak sınıflandırma yöntemlerine katkı sağlar. İndeks tabanlı yöntemler çoğunlukla tek sınıf ayrımına odaklanırken, peyzajlarda çoklu sınıfların ayrıştırılması gerekmektedir. Bu çalışmada, Sentinel-2 MSI görüntülerinden türetilen çeşitli spektral indekslerin nesne tabanlı görüntü sınıflandırma tekniği ile geniş alanlarda nasıl kullanılabileceği ortaya konulmuştur. Örneklem alanı olarak Mersin ili Silifke ilçesi seçilmiştir. Normalize Edilmiş Fark Vejetasyon İndeksi (NDVI), Normalize Edilmiş Fark Su İndeksi (NDWI), Yapı Alanı Çıkarım İndeksi (BAEI), Yapı Alan İndeksi (BAI), Bant Oranı (BR28, BR38), Normalize Edilmiş Yapı Alan İndeksi (NBAI), Yeni Yapı İndeksi (NBI), Kent İndeksi (UI), Normalize Edilmiş Fark Toprak İşleme İndeksi (NDTI), Kırmızı Kenar Bazlı Normalize Edilmiş Fark Vejetasyon İndeksi (NDVIre) ve Dönüştürülmüş Normalize Fark Su İndeksi (MNDWI) kullanılmıştır. BR28, BR38, NBAI, NBI ve UI ile anlamlı sonuçlar elde edilemezken, diğer indekslerle 0.8815 kappa katsayısı ve %94.11 genel doğruluk oranı sağlanmıştır.

Anahtar Kelimeler

Nesne-tabanlı görüntü analizi, Spektral indisler, Sınıflandırma, En yakın komşu algoritması, Makine öğrenmesi

Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery

Öz

Land Cover-Land Use (LC/LU) classification provides data for effective management of environmental and ecological decisions at the landscape scale. In this process, Sentinel-2 Multi Spectral Imager (MSI) satellite images contribute to classification methods by facilitating information extraction with their high spectral resolution. While index-based methods mostly focus on the separation of single classes, landscapes require the separation of multiple classes. This study shows how different spectral indexes derived from Sentinel-2 MSI imagery can be used in large areas with the object-based image classification technique. The Silifke district of Mersin province was selected as a sample area. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Built-up Area Extraction Index (BAEI), Built-up Area Index (BAI), Band Ratio (BR28, BR38), Normalized Built-up Area Index (NBAI), New Building Index (NBI), Urban Index (UI), Normalized Difference Soil Tillage Index (NDTI), Red Edge Based Normalized Difference Vegetation Index (NDVIre) and Normalized Difference Water Index (MNDWI) were used. While no significant results were obtained with BR28, BR38, NBAI, NBI and UI, 0.8815 kappa coefficient of 0.8815 and overall accuracy rate of %94.11 were obtained with other indexes.

Anahtar Kelimeler

Object-based image analysis, Spectral indexes, Classification, Nearest neighbour algorithm, Machine learning

Kaynakça

Ali, U., Esau, T. J., Farooque, A. A., Zaman, Q. U., Abbas, F., & Bilodeau, M. F. (2022). Limiting the collection of ground truth data for land use and land cover maps with machine learning algorithms. ISPRS International Journal of Geo-Information, 11(6), 333. https://doi.org/10.3390/ijgi11060333
Bayburt, S. (2009). Uydu görüntülerinin piksel ve nesne tabanlı sınıflandırma sonuçlarının karşılaştırılması (Doğu Trakya bölgesi örneği) (Thesis no. 251917) [Master’s thesis, İstanbul Technical University]. Council of Higher Education Thesis Center.
Bhaskaran, S., Paramananda, S., & Ramnarayan, M. (2010). Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Applied Geography, 30, 650–665. https://doi.org/10.1016/j.apgeog.2010.01.009
Bhatt, A., Ghosh, S. K., & Kumar, A. (2018). Spectral indices based object oriented classification for change detection using satellite data. International Journal of System Assurance Engineering and Management, 9, 33–42. https://doi.org/10.1007/s13198-016-0458-7
Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B
Copernicus Browser (CB). (2024, March 1). Data sources: SENTINEL-2. https://browser.dataspace.copernicus.eu/
Da Silva, V. S., Salami, G., Da Silva, M. I. O., Silva, E. A., Junior, J. J. M., & Alba, E. (2020). Methodological evaluation of vegetation indexes in land use and land cover (LULC) classification. Geology, Ecology, and Landscapes, 4(2), 159–169. https://doi.org/qg99
Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing, 8(4), 354. https://doi.org/10.3390/rs8040354
Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, 259–272. https://doi.org/10.1016/j.rse.2011.11.020

Esetlili, M. T., Balcık, F. B., Şanlı, F. B., Üstüner, M., Kalkan, K., Göksel, Ç., Gazioğlu, C., & Kurucu, Y. (2018). Comparison of object and pixel-based classifications for mapping crops using RapidEye imagery: A case study of Menemen Plain, Turkey. International Journal of Environment and Geoinformatics (IJEGEO), 5(2), 231–243. https://doi.org/10.30897/ijegeo.442002
Flanders, D., Hall-Beyer, M., & Pereverzoff, J. (2003). Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Canadian Journal of Remote Sensing, 29(4), 441–452. https://doi.org/10.5589/m03-006
Gaitan, C. F., Hsieh, W. W., Cannon, A. J., & Gachon, P. (2014). Evaluation of linear and non-linear downscaling methods in terms of daily variability and climate indices: surface temperature in Southern Ontario and Quebec, Canada. Atmosphere-Ocean, 52(3), 211–221. https://doi.org/10.1080/07055900.2013.857639
Ghorbanpour, A. K., Hessels, T., Moghim, S., & Afshar, A. (2021). Comparison and assessment of spatial downscaling methods for enhancing the accuracy of satellite-based precipitation over Lake Urmia Basin. Journal of Hydrology, 596, 126055. https://doi.org/10.1016/j.jhydrol.2021.126055
Grimm, R., Behrens, T., Märker, M., & Elsenbeer, H. (2008). Soil organic carbon concentrations and stocks on Barro Colorado Island - Digital soil mapping using Random Forests analysis. Geoderma, 146(1–2), 102–113. https://doi.org/10.1016/j.geoderma.2008.05.008
Harrak, Y., Rachid, A., & Aguejdad, R. (2025). Evaluation of spectral indices and global thresholding methods for the automatic extraction of built-up areas: An application to a semi-arid climate using Landsat 8 imagery. Urban Science, 9(3), 78. https://doi.org/10.3390/urbansci9030078
Hong, S.-H., Hendrickx, J. M. H., & Borchers, B. (2011). Down-scaling of SEBAL derived evapotranspiration maps from MODIS (250 m) to Landsat (30 m) scales. International Journal of Remote Sensing, 32(21), 645–6477. https://doi.org/10.1080/01431161.2010.512929
Jawak, S. D., Devliyal, P., & Luis, A. J. (2015). A comprehensive review on pixel oriented and object oriented methods for information extraction from remotely sensed satellite images with a special emphasis on cryospheric applications. Advances in Remote Sensing, 4(3), 177–195. http://doi.org/10.4236/ars.2015.43015
Jieli, C., Manchun, L., Yongxue, L., Chenglei, S., & Wei, H. (2010). Extract residential areas automatically by new built-up index. In 2010 18th International Conference on Geoinformatics (Vol. 3, pp. 171–175). IEEE. http://doi.org/10.1109/GEOINFORMATICS.2010.5567823
Kalkan, K., & Maktav, D. (2010). Comparison of object-based and pixel-based classification methods [Symposium paper]. In T. Kavzaoğlu & D. Maktav (Eds.), III. Remote Sensing and Geographic Information Systems Symposium (pp. 153–160). Gebze-Kocaeli.
Karakus, P., Karabork, H., & Kaya, S. (2017). A comparison of the classification accuracies in determining the land cover of Kadirli Region of Turkey by using the pixel based and object based classification algorithms. International Journal of Engineering and Geosciences (IJEG), 2(2), 52–60. http://doi.org/10.26833/ijeg.298951
Kaufman, Y. J. (1987). Atmospheric effect on spectral signature – measurements. Advances in Space Research, 7(11), 203–206. https://doi.org/10.1016/0273-1177(87)90313-9
Kawamura, M., Jayamanna, S., & Tsujiko, Y. (1996). Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data. International Archives of Photogrammetry and Remote Sensing, 31, 321–326.
Kebede, T. A., Hailu, B. T., & Suryabhagavan, K. V. (2022). Evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries: A case of Addis Ababa city, Ethiopia. Environmental Challenges, 8, 100568. https://doi.org/10.1016/j.envc.2022.100568
Lamichhane, S., Kumar, L., & Wilson, B. (2019). Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review. Geoderma, 352, 395–413. https://doi.org/10.1016/j.geoderma.2019.05.031
Lemenkova, P., & Debeir, O. (2023). Computing vegetation indices from the satellite images using GRASS GIS scripts for monitoring mangrove forests in the coastal landscapes of Niger Delta, Nigeria. Journal of Marine Science and Engineering, 11(4), 871. https://doi.org/10.3390/jmse11040871
Li, C., Shao, Z., Zhang, L., Huang, X., & Zhang, M. (2021). A comparative analysis of index-based methods for impervious surface mapping using multiseasonal Sentinel-2 satellite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3682–3694. https://doi.org/10.1109/jstars.2021.3067325
Magpantay, A. T., Adao, R. T., Bombasi, J. L., Lagman, A. C., Malasaga, E. V., & Ye, C.-S. (2019). Analysis on the effect of spectral index images on improvement of classification accuracy of Landsat-8 OLI image. Korean Journal of Remote Sensing, 35(4), 561–571. https://doi.org/10.7780/kjrs.2019.35.4.6
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), 1145–1161. https://doi.org/10.1016/j.rse.2010.12.017
Nandam, V., & Patel, P. L. (2021). A novel hybrid approach using SVM and spectral indices for enhanced land use land cover mapping of coastal urban plains. Geocarto International, 37(16), 4714–4736. https://doi.org/10.1080/10106049.2021.1899300
NASA (2025, February 26). Earth data Sentinel-2 MSI. https://tinyurl.com/4dacb7ar
Newman, M. E., McLaren, K. P., & Wilson, B. S. (2011). Comparing the effects of classification techniques on landscape-level assessments: pixel-based versus object-based classification. International Journal of Remote Sensing, 32(14), 4055–4073. https://doi.org/10.1080/01431161.2010.484432
Nichols, T. R., Wisner, P. M., Cripe, G., & Gulabchand, L. (2010). Putting the kappa statistic to use. The Quality Assurance Journal, 3(3–4), 41–77. https://doi.org/10.1002/qaj.481
Nussbaum, S., & Menz, G. (2008). eCognition image analysis software. In T. Blaschke, S. Lang, & G. J. Hay (Eds.), Object-based image analysis and treaty verification (pp. 129–143). Springer. https://doi.org/10.1007/978-1-4020-6961-1_3
Nwagoum, C. S. K., Yemefack, M., Tedou, F. B. S., & Oben, F. T. (2023). Sentinel-2 and Landsat-8 potentials for high-resolution mapping of the shifting agricultural landscape mosaic systems of southern Cameroon. International Journal of Applied Earth Observation and Geoinformation, 124, 103545. https://doi.org/10.1016/j.jag.2023.103545
Orman Genel Müdürlüğü (OGM). (2021). E-Harita Uygulaması- Meşçere. https://cbs.ogm.gov.tr/vatandas/. Accessed February 16, 2021.
Osgouei, P. E., Kaya, S., Sertel, E., & Alganci, U. (2019). Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery. Remote Sensing, 11, 345. https://doi.org/10.3390/rs11030345
Ouchra, H., Belangour, A., & Erraissi, A. (2022). A comparative study on pixel-based classification and object-oriented classification of satellite image. International Journal of Engineering Trends and Technology, 70(8), 206–215. https://doi.org/10.14445/22315381/ijett-v70i8p221
Özgür, D. (2023, March 3). Coğrafi bilgi sistemleri verileri, Türkiye sayısallaştırılmış yükselti modeli (dijital yükseklik modeli (DEM)). https://www.ozgurcografya.com/
Öztürk, D. (2022). Sentinel-2A MSI ve Landsat-9 OLI-2 görüntüleri kullanılarak farklı geçirimsiz yüzey indekslerinin karşılaştırmalı değerlendirmesi: Samsun örneği. Ege Coğrafya Dergisi, 31(2), 401–423. https://doi.org/10.51800/ecd.1175827
Pandey, D., & Tiwari, K. C. (2020). Extraction of urban built-up surfaces and its subclasses using existing built-up indices with separability analysis of spectrally mixed classes in AVIRIS-NG imagery. Advances in Space Research, 66(8), 1829–1845. https://doi.org/10.1016/j.asr.2020.06.038
Rahar, P. S., & Pal, M. (2020). Comparison of various indices to differentiate built-up and bare soil with Sentinel 2 Data. In J. K. Ghosh & I. da Silva (Eds.), Applications of Geomatics in Civil Engineering (pp. 501–509). Springer, Singapore. https://doi.org/10.1007/978-981-13-7067-0_39
Rastner, P., Bolch, T., Notarnicola, C., & Paul, F. (2014). A comparison of pixel- and object-based glacier classification with optical satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(3), 853–862. https://doi.org/10.1109/jstars.2013.2274668
Reddy, B. B. K., Maragatham, S., Santhi, R., Balachandar, D., Vijayalakshmi, D., Vasu, D., & Gopalakrishnan, M. (2024). Predictive soil mapping using random forest models: Applications in pH and soil organic matter assessment. Plant Science Today, 11(4), 1–12. https://doi.org/10.14719/pst.3865
Rouibah, K. (2023). The use of bands ratio derived from Sentinel 2 imagery to detect built up area in the dry period (North East Algeria). Applied Geomatics, 15, 473–482. https://doi.org/10.1007/s12518-023-00513-y
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the great plains with ERTS [Symposium paper]. In S. C. Freden & E. P. Mercanti (Eds.), Proceedings of the Third Earth Resources Technology Satellite-1 Symposium (pp. 309–317). Washington, DC.
Sentiwiki. (2025, March 26). Sentinel-2 Products MSI Products Overview. https://sentiwiki.copernicus.eu/web/s2-products
Story, M., & Congalton, R. G. (1986). Accuracy assessment: a user’s perspective. Photogrammetric Engineering and Remote Sensing, 52(3), 397–399.
Suharyadi, R., Umarhadi, D. A., Awanda, D., & Widyatmanti, W. (2022). Exploring built-up indices and machine learning regressions for multi-temporal building density monitoring based on Landsat series. Sensors, 22, 4716. https://doi.org/10.3390/s22134716
Tehrany, M. S., Pradhan, B., & Jebuv, M. N. (2014). A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto International, 29(4), 351–369. https://doi.org/10.1080/10106049.2013.768300
Tonbul, H., & Kavzoğlu, T. (2018). Nesne Tabanlı Görüntü Analizinde Görüntü Bölütleme Yaklaşımları ve Bölütleme Kalitesinin Analizi. Harita Dergisi, 160, 12–23. https://doi.org/10.15659/uzalcbs2018.6403
Torres-Sanchez, J., Lopez-Granados, F., & Pena, J. M. (2015). An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, 114, 43–52. https://doi.org/f7jhbp
Trimble. (2025, March 26). eCognition suite documentation. https://docs.ecognition.com
TÜİK (2023, April 12). İl ve ilçe merkezlerine göre nüfus ve yüzölçümü verileri. https://bit.ly/4pw7Dh2
Weichert, A., & Bürger, G. (1998). Linear versus nonlinear techniques in downscaling. Climate Research, 10, 83–93. https://doi.org/10.3354/cr010083
Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), 884–893. https://doi.org/10.1016/j.jag.2011.06.008
Woolson, R. F., Bean, J. A., & Rojas, P. B. (1986). Sample size for case-control studies using Cochran's statistic. Biometrics, 42(4), 927–932. https://doi.org/10.2307/2530706
Wu, B., Zheng, H., Xu, Z., Wu, Z., & Zhao, Y. (2022). Forest burned area detection using a novel spectral index based on multi-objective optimization. Forests, 13(11), 1787–1807. https://doi.org/10.3390/f13111787
Xi, Y., Thinh, N. X., & Li, C. (2019). Preliminary comparative assessment of various spectral indices for built-up land derived from Landsat-8 OLI and Sentinel-2A MSI imageries. European Journal of Remote Sensing, 52(1), 240–252. https://doi.org/10.1080/22797254.2019.1584737
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987
Zhang, K., Chen, Y., Wang, W., Wu, Y., Wang, B., & Yan, Y. (2023). A method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination. Geocarto International, 38, 2158948. https://doi.org/10.1080/10106049.2022.2158948
Zhang, T.-X., Su, J.-Y., Liu, C.-J., & Chen, W.-H. (2019). Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture. International Journal of Automation and Computing, 16(1), 16–26. https://doi.org/10.1007/s11633-018-1143-x

Ayrıntılar

Birincil Dil

İngilizce

Konular

Peyzaj Mimarlığında Bilgisayar Teknolojileri, Peyzaj Yönetimi, Peyzaj Mimarlığı (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Esin Karamanlı ^*
0000-0003-0324-1486
Türkiye

Ömer Faruk Uzun
0000-0002-8541-4098
Türkiye

Yayımlanma Tarihi

30 Aralık 2025

Gönderilme Tarihi

14 Nisan 2025

Kabul Tarihi

11 Kasım 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 21 Sayı: 2

DOI

https://doi.org/10.58816/duzceod.1675848

IZ

https://izlik.org/JA72CP46JM

APA

Karamanlı, E., & Uzun, Ö. F. (2025). Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi, 21(2), 66-81. https://doi.org/10.58816/duzceod.1675848

AMA

1.Karamanlı E, Uzun ÖF. Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery. DÜOD. 2025;21(2):66-81. doi:10.58816/duzceod.1675848

Chicago

Karamanlı, Esin, ve Ömer Faruk Uzun. 2025. “Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery”. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi 21 (2): 66-81. https://doi.org/10.58816/duzceod.1675848.

EndNote

Karamanlı E, Uzun ÖF (01 Aralık 2025) Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi 21 2 66–81.

IEEE

[1]E. Karamanlı ve Ö. F. Uzun, “Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery”, DÜOD, c. 21, sy 2, ss. 66–81, Ara. 2025, doi: 10.58816/duzceod.1675848.

ISNAD

Karamanlı, Esin - Uzun, Ömer Faruk. “Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery”. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi 21/2 (01 Aralık 2025): 66-81. https://doi.org/10.58816/duzceod.1675848.

JAMA

1.Karamanlı E, Uzun ÖF. Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery. DÜOD. 2025;21:66–81.

MLA

Karamanlı, Esin, ve Ömer Faruk Uzun. “Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery”. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi, c. 21, sy 2, Aralık 2025, ss. 66-81, doi:10.58816/duzceod.1675848.

Vancouver

1.Esin Karamanlı, Ömer Faruk Uzun. Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery. DÜOD. 01 Aralık 2025;21(2):66-81. doi:10.58816/duzceod.1675848

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Sentinel 2 MSI Uydu Görüntülerini Kullanarak Spektral İndeks Çıkarımına Dayalı Peyzaj Düzeyinde Nesne Tabanlı Görüntü Sınıflandırma Süreci

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Anahtar Kelimeler

Object-Based Image Classification Process at Landscape Level Based on Spectral Index Extraction Using Sentinel 2 MSI Satellite Imagery

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

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Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

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