LAND USE AND LAND COVER CLASSIFICATION USING HYPERSPECTRAL IMAGERY: EVALUATING THE PERFORMANCE OF SPECTRAL ANGLE MAPPER, SUPPORT VECTOR MACHINE AND RANDOM FOREST

24 citations as recorded by crossref.

1. Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases L. Alonso et al. 10.3390/rs12142276
2. Overview of Recent Developments in Flood Mitigation Techniques with Respect to Indian Subcontinent P. Pusdekar & S. Dudul 10.32628/IJSRST218356
3. Land use change and climate dynamics in the Rift Valley Lake Basin, Ethiopia A. Ayalew et al. 10.1007/s10661-022-10393-1
4. Monitoring Forest Cover Dynamics Using Orthophotos and Satellite Imagery L. Blaga et al. 10.3390/rs15123168
5. Farklı Platformlardan Elde Edilen Hiperspektral Görüntülerin Sınıflandırılmasında Evrişimli Sinir Ağları, Destek Vektör Makineleri ve Rastgele Orman Algoritmalarının Performanslarının Karşılaştırılması E. SEYREK & M. UYSAL 10.35414/akufemubid.1177912
6. Land use/land cover (LULC) classification using hyperspectral images: a review C. Lou et al. 10.1080/10095020.2024.2332638
7. Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and Classification M. Imani & D. Cerra 10.3390/rs17091623
8. Clasificación de uso y cobertura del suelo a través de algoritmos de aprendizaje automático: revisión bibliográfica R. Tobar-Díaz et al. 10.4995/raet.2023.19014
9. A novel multi-class land use/land cover classification using deep kernel attention transformer for hyperspectral images G. Tejasree & A. L 10.1007/s12145-023-01109-1
10. IDA: Improving distribution analysis for reducing data complexity and dimensionality in hyperspectral images D. AL-Alimi et al. 10.1016/j.patcog.2022.109096
11. Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting M. Chowdhury 10.1016/j.envc.2023.100800
12. Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification E. Seyrek & M. Uysal 10.15446/esrj.v28n2.105296
13. An extensive review of hyperspectral image classification and prediction: techniques and challenges G. Tejasree & L. Agilandeeswari 10.1007/s11042-024-18562-9
14. Obtaining LULC distribution at 30-m resolution from Pixxel’s first technology demonstrator hyperspectral imagery G. Petropoulos et al. 10.1080/01431161.2024.2370504
15. Accuracy and processing speed trade-offs in classical and quantum SVM classifier exploiting PRISMA hyperspectral imagery R. Shaik & S. Periasamy 10.1080/01431161.2022.2061877
16. Time Series Remote Sensing Image Classification Using Feature Relationship Learning P. Dou et al. 10.1109/TGRS.2024.3386171
17. Ground-Based Hyperspectral Image Surveillance System for Explosive Detection: Methods, Experiments, and Comparisons M. Kütük et al. 10.1109/JSTARS.2023.3299730
18. Estimating afforestation related forest cover change using data fusion and machine learning K. Khan et al. 10.1088/2515-7620/ad88e0
19. A Systematic Review on Advancements in Remote Sensing for Assessing and Monitoring Land Use and Land Cover Changes Impacts on Surface Water Resources in Semi-Arid Tropical Environments M. Mashala et al. 10.3390/rs15163926
20. Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data M. Ahmad et al. 10.1007/s11356-022-24442-2
21. Land use/land cover (LULC) classification using deep-LSTM for hyperspectral images G. Tejasree & L. Agilandeeswari 10.1016/j.ejrs.2024.01.004
22. ETR: Enhancing transformation reduction for reducing dimensionality and classification complexity in hyperspectral images D. AL-Alimi et al. 10.1016/j.eswa.2022.118971
23. Erosivity factor effect assessment in the Mediterranean coastal watersheds: a Moroccan case study O. Rami et al. 10.1007/s12517-022-10178-3
24. Hyperspectral fluorescence imaging: Robust detection of petroleum in porous sedimentary rock formations O. Gadea & S. Khan 10.1190/INT-2020-0140.1