TEXTURE ANALYSIS FOR LAND USE LAND COVER (LULC) CLASSIFICATION IN PARTS OF AHMEDABAD, GUJARAT

6 citations as recorded by crossref.

1. Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal P. Mohammadpour et al. 10.3390/rs14184585
2. The use of texture analysis and band transformation on multispectral imagery to map open-pit mines using machine learning G. Nugroho et al. 10.1088/1755-1315/1109/1/012071
3. A New Texture Aware—Seed Demand Enhanced Simple Non-Iterative Clustering (ESNIC) Segmentation Algorithm for Efficient Land Use and Land Cover Mapping on Remote Sensing Images R. Selvaraj & D. Amali 10.1109/ACCESS.2024.3519612
4. Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta C. Orieschnig et al. 10.1080/22797254.2021.1948356
5. Improving Open-Pit Mining Mapping Accuracy in the Tropics Using Enhanced Input Selection for Classification Process of Machine Learning G. Nugroho et al. 10.1007/s12524-023-01783-0
6. Evaluation of Sentinel-1 Interferometric SAR Coherence efficiency for Land Cover Mapping M. Soleimani et al. 10.52547/jgit.9.3.85