Machine learning algorithms for objective-based satellite image classification and land cover accuracy prediction

• Main Authors: Al-Hameedawi, Amjed Naser, Shihab, Tay H.
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Artificial neural networks; Image classification; Image enhancement; Land cover; Land surface temperature; Machine learning; Satellite imagery; Satellite observation; Support vector machines; Vegetation
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0236422

This paper explores the assessment of Diwaniyas Land Surface Temperature (LST) and the changes, in landcover from 2015 to 2021. Due to the challenges of obtaining information through ground based measurements using satellite measurements in the thermal infrared range is seen as a highly attractive option. The findings indicate that LST has increased as a result of urbanization decreased areas and expansion of land. It was observed that regions with vegetation experienced LST while areas with more vegetation had lower LST. These findings can contribute to the formulation of an inclusive climate resilience policy. Enhance Diwaniyas sustainability in the face of climate change impacts. Artificial neural networks were employed in this research to establish rulesets at the object level. By utilizing a Landsat image, network and Support Vector Machine techniques a detailed map of Diwaniya was generated. The accuracy of the maps classification was determined to be 94% with a kappa index value of 0.90. These outcomes demonstrate the effectiveness of the proposed classification method and its potential to offer a model, for applications.