Comparison of support vector machine and object based classification methods for coastline detection

20 citations as recorded by crossref.

1. A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means F. Figliomeni et al. https://doi.org/10.3390/rs15123181
2. Comparison of Pixel-Based and Object-Based Classification Methods in Determination of Wetland Coastline A. Dervisoglu et al. https://doi.org/10.30897/ijegeo.666185
3. Climate induced Coastline Changes: A case study in Togo (West Africa) Z. Attiq et al. https://doi.org/10.33411/IJIST/2021030104
4. A coastline automatic extraction and classification method by using multi-source remote sensing and DEM data D. Chai et al. https://doi.org/10.1016/j.rsma.2025.104450
5. Comparison of Pixel-Based and Object-Based Classification Methods in Determination of Wetland Coastline A. Dervişoğlu et al. https://doi.org/10.30897/ijegeo.713307
6. Automatic Regionalized Coastline Extraction Method Based on High-Resolution Images Z. Lu et al. https://doi.org/10.1109/ACCESS.2024.3356710
7. Burdur Gölü Kıyı Şeridindeki Değişiminin Uzaktan Algılama ile Haritalanması A. SABUNCU https://doi.org/10.35414/akufemubid.711653
8. Shoreline change assessment using multi-temporal satellite images: a case study of Lake Sapanca, NW Turkey U. Duru https://doi.org/10.1007/s10661-017-6112-2
9. An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images F. Erdem et al. https://doi.org/10.1016/j.asr.2020.10.043
10. Machine learning application in modelling marine and coastal phenomena: a critical review A. Pourzangbar et al. https://doi.org/10.3389/fenve.2023.1235557
11. Long-Term Shoreline Change Analysis using Optical Satellite Images of the East Coast of the Korean Peninsula M. Yun et al. https://doi.org/10.26748/KSOE.2025.029
12. SEA-LAND SEGMENTATION MODELS IN DEEP LEARNING FROM REMOTE SENSING DATA R. OKHRIMCHUK et al. https://doi.org/10.17721/1728-2713.107.15
13. An Overview of Coastline Extraction from Remote Sensing Data X. Zhou et al. https://doi.org/10.3390/rs15194865
14. Ladik Gölü’nde Kıyı Şeridi ve Su Alanı Değişimlerinin Uzaktan Algılama Tekniği ile Belirlenmesi Ö. UZUN & B. OLGUN https://doi.org/10.51765/tayod.1287426
15. Advances in Remote Sensing and Deep Learning in Coastal Boundary Extraction for Erosion Monitoring M. Blais & M. Akhloufi https://doi.org/10.3390/geomatics5010009
16. Coastline Change Modelling Induced by Climate Change Using Geospatial Techniques in Togo (West Africa) Y. Konko et al. https://doi.org/10.4236/ars.2020.92005
17. Advances in Shoreline Detection using Satellite Imagery T. Kang et al. https://doi.org/10.7837/kosomes.2023.29.6.598
18. Aktarımlı Öğrenme ile SENTINEL-2 Görüntülerinden Kıyı Çizgisi Bölütlemesi S. KARAGÖL et al. https://doi.org/10.51489/tuzal.830052
19. Satellite Reveals the Accelerated Coastline Erosion of Sydney’s Sandy Beaches Since the 21st Century M. Wang et al. https://doi.org/10.1080/07038992.2024.2432418
20. Deep learning–based water body extraction using high-resolution RGB-UAV imagery: a case study on Horseshoe Island, Antarctica M. Selbesoğlu et al. https://doi.org/10.1007/s10661-026-15519-3