RETRACTED ARTICLE: Pre-processing on remotely sensed data with unsupervised classification analysis

Abstract

The use of remote sensing and geographic information system (GIS) technologies grow drastically in recent years by ecologists around the globe. At present, using sophisticated sensors, there is a massive challenge in handling the high dense remotely captured information with spatial, spectral, temporal, and radiometric resolutions. This article addresses how to handle such large volume remotely sensed data using R programming with the aid of RStudio. We aim broadly two categories, such as image preprocessing and classification techniques on remotely sensed data. Image preprocessing methods such as false-color composite, pan-sharpening, single event upset error mitigation, and a view on the hyper spectral image. The other category comes with a focus on landscape spatial features analysis and unsupervised classification to analyze vegetation land. The CLARA (Clustering LARge Application) algorithm is used in this study which exploits k-medoids approaches for the unsupervised classification. Also, for results comparison, different vegetation indices such as normalized difference water index (NDWI), modified NDWI (MNDWI), and soil adjusted vegetation index are used for vegetation (land) analysis. Also, for the unsupervised classification, the Silhouette index is used to compare the clustering algorithms.