2026-06-27T22:46:01Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/00008999 2023-08-14T06:03:39Z 1607959986665 1607959986665:1608033738939

IMAGE ANALYSIS OF UNSUPERVISED CLASSIFICATION IN LAND COVER TYPES BY GIS TECHNIQUE: SOUTHERN YANGON REGION Win Thet Myint This research approach image classification using Arc GIS from satellite Landsat image. Supervised Learning predicts based on a class type. Unsupervised Learning discovers underlying patterns. Image classification is small piece of the very intricate machine learning. In addition to classification, other key aspects include object detection and object localization. This is followed by the extended methodological workflow supported by illustrative print screens and technical description of data processing in ArcGIS. The methodology includes a workflow involving several technical steps of raster data processing in ArcGIS: 1. coordinate projecting, 2. pre-processing, 3. inspection of raster statistics, 4. spectral bands combination, 5. calculations, 6. unsupervised classification, 7. mapping. The classification was done by clustering technique using ISO Cluster algorithm Classification. The main purposes of this research are to interpret changing the area of the land use land cover in the interest study area using the Unsupervised Classification method, Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built Up Index (NDBI) approaches. This paper finally presents the results of the ISO Cluster application for Landsat (8) image processing and concludes final remarks on the perspectives of environmental mapping based on Landsat (8) image processing in ArcGIS. 2023-03-01 https://meral.edu.mm/records/8999