Unsupervised Landcover Classification In SNAP Using Sentinel 1 Imagery

Introduction

Synthetic Aperture Radar, or SAR, is a method of RADAR imaging that propagates a microwave signal to create an image over an area. The image is generates based on the type of backscatter that occurs when the signal

hits a target. Unlike optical remote sensing, SAR is not affected by clouds or time of day; however, it can be adversely affected by heavy precipitation. This tutorial will use SAR data and image processing software to classify a RADAR image using an unsupervised classification method.

Unsupervised Classification

Unsupervised classification aggregates spectral classes, or clusters, in a multiband image based on pixel value alone. This differs from supervised classification, where the user provides training data that the algorithm can use to assess the class to which a pixel belongs.

For unsupervised classification, the user instead tells the image classifier how many “clusters”, or classes, are desired, and what method to use.

In this tutorial, we will be using a K-Means classifier for the clustering algorithm.

K-means is an algorithm that combines “observations” (in this case, pixels) into discrete

groups. It does this by creating nodes, which represent the center of a data cluster. This node is positioned so that the n distance between it and the nearest points is on average smaller than the distance between those points and the next node (Hunt-Walker & Ewing, 2014).