OBJECT-BASED APPROACHES FOR LAND USE-LAND COVER CLASSIFICATION USING HIGH RESOLUTION QUICK BIRD SATELLITE IMAGERY (A CASE STUDY: KERBELA, IRAQ)

• Published in: Geodesy and cartography (Vilnius) Vol. 48; no. 2; pp. 85 - 91
• Main Authors: Jaber, Hussein Sabah, Shareef, Muntadher Aidi, Merzah, Zainab Fahkri
• Format: Journal Article
• Language: English
• Published: Vilnius Vilnius Gediminas Technical University 29-06-2022
• Subjects: Classification; High resolution; Image classification; Image enhancement; Image resolution; Land cover; Land use; Land use classification; LULC; maximum likelihood; Methods; object-based approaches; Pixels; processing; quick bird; Resolution; Satellite imagery; Iraq
• ISSN: 2029-6991; 2029-7009
• DOI: 10.3846/gac.2022.14453

Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that use of textural data during the object image classification approach can considerably enhance land use classification performance. Moreover, the results showed higher overall accuracy (86.02%) in the o object based method than pixel based (79.06%) in urban extractions. The object based performed much more capabilities than pixel based.