Abstract
Adopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion plays a big role to map the Land Use Land Cover. Therefore, This study aims to find out a refining method for the Land Use Land Cover estimating using these steps; (1) applying a three pan-sharpening fusion approaches to combine panchromatic imagery that has high spatial resolution with multispectral imagery that has low spatial resolution, (2) employing five pixel-based classifier approaches on multispectral imagery and fused images; artificial neural net, support vector machine, parallelepiped, Mahalanobis distance and spectral angle mapper, (3) make a statistical comparison between image classification results. The Landsat-8 image was adopted for this research. There are twenty Land Use Land Cover thematic maps were generated in this study. A suitable and reliable Land Use Land Cover method was presented based on the most accurate results. The results validation was performed by adopting a confusion matrix method. A comparison made between the images classification results of multispectral imagery and all fused images levels. It proved the Land Use Land Cover map produced by Gram–Schmidt Pan-sharpening and classified by support vector machine method has the most accurate result among all other multispectral imagery and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the spectral angle mapper algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions.
Highlights
An accurate thematic map of Land Use Land Cover (LULC) plays a big role in different remote sensing applications such as; change detection, environment managing and monitoring, LULC detection, hazard prediction, urban area expansion, forest monitoring and other (Sang et al 2014; Khatami et al 2016; Dibs et al 2017; Zhang et al 2018; Karar et al 2020)
The Landsat-8 MS images classified with a five pixelbased classifier approaches (PP, artificial neural net (ANN), support vector machine (SVM), spectral angle mapper (SAM), and Mahalanobis distance (Mah)), with selecting the same training and testing sites to conduct each classification algorithm and to give a good result reflections when perform a statistical comparison in the step of this research
The classification outputs reveal that the accuracy obtained from adopting the SVM approach provides the highest results, the overall accuracy of about (99.85%) with a kappa coefficient of about (0.98) from image classification
Summary
An accurate thematic map of Land Use Land Cover (LULC) plays a big role in different remote sensing applications such as; change detection, environment managing and monitoring, LULC detection, hazard prediction, urban area expansion, forest monitoring and other (Sang et al 2014; Khatami et al 2016; Dibs et al 2017; Zhang et al 2018; Karar et al 2020). Remote sensing is a powerful tool and very useful for mapping the LULC from using a suitable satellite images with a good selecting of classification method. Image classification approaches consider as the best method to monitor, manage and estimate the LULC (Dibs 2013; Sang et al 2014). To perform classification, it needs to involve different stages such as selection training and testing samples, atmospheric correction, radiometric correction, geometric correction, objects extraction, classifier method selection, post-classification process, and performing results validation (Singh et al 2014; Dixon et al 2015; Hayder et al 2018; Dibs 2018). For LULC estimating there are large numbers of techniques and methodologies to apply, some of these classifiers under pixel-based and other under object-based, these algorithms such as the artificial neural net (ANN), support vector machine (SVM), parallelepiped (PP), Mahalanobis distance (Mah) and spectral angle mapper (SAM), Decision
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