Object-Based Approaches for Land Use-Land Cover Classification Using High Resolution Quick Bird Satellite Imagery (A Case Study: Kerbela, Iraq)

Abstract

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.

Machine learning based combinatorial analysis for land use and land cover assessment in Kyiv City (Ukraine)

“…This classification is conducted using multi-sensor features, including temperature, night-time light, backscattering, topography, optical spectra, and NDVI, EVI, NDWI indices time-series metrics 34 and synthetically incorporating geometry, and texture data. 13,25,50,53 One of the main objectives of our study was to investigate the performance of different machine learning algorithms RF and SVM for the classification of LULC on the GEE platform. Thus, the RF-based LULC classification is more accurate than SVM according to OA and Kappa (Table 14).…”

“…For an effective distinction between built-up area and bare land, the authors 49 suggest RF. This classification is conducted using multi-sensor features, including temperature, night-time light, backscattering, topography, optical spectra, and NDVI, EVI, NDWI indices time-series metrics 34 and synthetically incorporating geometry, and texture data 13 , 25 , 50 , 53 …”

“…However, it is desirable to use high-resolution images such as WorldView-2 and Spot-5 in order to achieve the required accuracy, which is not always possible. [52][53][54][55] The main aim of the present study was to analyze ways to improve the accuracy of the LULC classification with machine learning algorithms RF and SVM based on multispectral Sentinel-2 satellite images. The decisive technical novelty of the study is that four spectral combinations were proposed.…”

“…However, it is desirable to use high-resolution images such as WorldView-2 and Spot-5 in order to achieve the required accuracy, which is not always possible 52 – 55 …”