Influences Combination of Multi-Sensor Images on Classification Accuracy

TL;DR

This study evaluates how combining multi-sensor remote sensing images affects classification accuracy, comparing different classifiers and demonstrating that fused images and Euclidean classifier yield superior results.

Contribution

It investigates the impact of image fusion on classification accuracy and identifies the Euclidean classifier as the most effective among four tested classifiers.

Findings

Fused images outperform original multispectral images in classification accuracy.

Euclidean classifier provides more robust results than other classifiers.

Fusion enhances the accuracy of supervised classification methods.

Abstract

This paper focuses on two main issues; first one is the impact of combination of multi-sensor images on the supervised learning classification accuracy using segment Fusion (SF). The second issue attempts to undertake the study of supervised machine learning classification technique of remote sensing images by using four classifiers like Parallelepiped (Pp), Mahalanobis Distance (MD), Maximum-Likelihood (ML) and Euclidean Distance(ED) classifiers, and their accuracies have been evaluated on their respected classification to choose the best technique for classification of remote sensing images. QuickBird multispectral data (MS) and panchromatic data (PAN) have been used in this study to demonstrate the enhancement and accuracy assessment of fused image over the original images using ALwassaiProcess software. According to experimental result of this study, is that the test results indicate the supervised classification results of fusion image, which generated better than the MS did. As well as the result with Euclidean classifier is robust and provides better results than the other classifiers do, despite of the popular belief that the maximum-likelihood classifier is the most accurate classifier.