Abstract
Two common techniques for classifying satellite imagery are pixel-based and Feature extraction image analysis methods. Typically, for agreements reached imaging, pixel-based analysis is used, whereas high-resolution imagery is suitable for Feature extraction analysis. However, In the classification of moderate images, image segmentation's ability depending on criteria such as shape, color, texture, and spatial features in Feature extraction image analysis implies it can perform better than pixel-based analysis. A comparative study of the two methods was performed using Sentinel-2 imagery from 18 May 2020 to categorize LU/LC in the City of Baghdad, Iraq. After calculating LU/LC for Baghdad images' capital, a supervised classification was performed using the two methods. The images used have been the support vector machines (SVM) and the maximum likelihood classification (MLC) for pixel-based method and Feature extraction method, which is available in ENVI and ArcGIS software packages, respectively. Land cover and land use classes included five Groups (vegetation area, asphalt road, soil area, water body, and built-up) was found that the Feature extraction methodology produced higher overall accuracy and Kappa index in the city of Baghdad image. The highest achieved accuracy for the Feature extraction technique was overall accuracy 95% with Kappa index 0.94 of SVM and overall accuracy of 92% with Kappa index 0.90 of MLC. In comparison, the highest accuracy for the pixel-based was overall accuracy 88% with Kappa index 0.84 of SVM and overall accuracy 86% with Kappa index 0.82 of MLC.
Highlights
IntroductionQualitative and quantitative information is given by satellite and remote sensing images that reduce fieldwork and study time complexity
In supplying and producing geospatial data, satellite images play a vital role
Using Envi software, Training sites collected were chosen to be the region of interest (ROIs) polygons for each group and used the following ROIs to classify the thematic map of LU/ LC for the study region
Summary
Qualitative and quantitative information is given by satellite and remote sensing images that reduce fieldwork and study time complexity. Classification of the satellite image is considered a robust technique to collect data from many satellite images. There is a clear need for a similar comparison of the feature extraction and pixel-based classification of high-resolution satellite images using remote sensing, with many aspects relevant to general fieldwork. It is be referred to as satellite image classification as extracting data from satellite images. Satellite image classification is not very difficult, but in the processing of satellite images, the observer must make multiple decisions and choices. Satellite image classification involves interpreting remote sensing images and spatial data mining, studying the different classification types described in this research [1, 2]
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