Remote Sensing Data Classification Technique: A Review

Author:

Didore Vaibhav A.1,Nalawade Dhananjay B.1,Vaidya Renuka B.1

Affiliation:

1. Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India

Abstract

Remote sensing is the prominent technology to study the ecology of the earth. Classification is a commonly used technique for quantitative analysis of remote sensing image data. It is based on the concept of segmentation of spectral regions into regions that can be associated with a soil cover class of interest for a particular application. As an advanced remote sensing tool, Hyperspectral remote sensing technology has been studied in many applications such as geology, topography, biology, soil science, hydrology, plants and ecosystems, atmospheric science. In this paper, Supervised Decision tree; Minimum distance; Maximum likelihood classification; Parallelepiped; K-nearest neighbor; and Unsupervised K-mean; & ISODATA algorithm are reviewed. This review is helpful to the researchers who are studying this emerging field i.e. HRS.

Publisher

Naksh Solutions

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