Supervised Classification Performance of Multispectral Images

TL;DR

This paper evaluates various classification algorithms for remote sensing images, highlighting the effectiveness of the Mahalanobis classifier in accurately classifying multispectral data amidst increasing data complexity.

Contribution

The study compares supervised and unsupervised classification methods, identifying the Mahalanobis classifier as the most effective for multispectral remote sensing image classification.

Findings

Mahalanobis classifier outperforms other methods

Traditional algorithms face challenges with large spatiotemporal data

Experimentation with multiple classification techniques

Abstract

Nowadays government and private agencies use remote sensing imagery for a wide range of applications from military applications to farm development. The images may be a panchromatic, multispectral, hyperspectral or even ultraspectral of terra bytes. Remote sensing image classification is one amongst the most significant application worlds for remote sensing. A few number of image classification algorithms have proved good precision in classifying remote sensing data. But, of late, due to the increasing spatiotemporal dimensions of the remote sensing data, traditional classification algorithms have exposed weaknesses necessitating further research in the field of remote sensing image classification. So an efficient classifier is needed to classify the remote sensing images to extract information. We are experimenting with both supervised and unsupervised classification. Here we compare the different classification methods and their performances. It is found that Mahalanobis classifier performed the best in our classification.