Variable importance and random forest classification using RADARSAT-2 PolSAR data

Author

Hariharan, Siddharth ; Tirodkar, Siddhesh ; De, Shaunak ; Bhattacharya, Avik

Author_Institution

Centre of Studies in Resources Eng., IIT Bombay, Mumbai, India

fYear

2014

fDate

13-18 July 2014

Firstpage

1210

Lastpage

1213

Abstract

In this paper we have classified Polarimetric Synthetic Aperture Radar (PolSAR) data using the Random Forest (RF) classifier. The variables were ranked using the mean decrease in accuracy permutation method for each terrain class. RADARSAT-2 (RS-2) data acquired over Mumbai, India was used in this study. This technique is able to efficiently classify the dataset, as well as rank the parameters used in that classifier.

Keywords

geophysical image processing; geophysical techniques; image classification; radar polarimetry; remote sensing by radar; synthetic aperture radar; India; Mumbai; RADARSAT-2 PolSAR data; permutation method; polarimetric synthetic aperture radar; random forest classification; Accuracy; Correlation; Entropy; Radio frequency; Scattering; Support vector machines; Vegetation; Polarimetry; Random Forest Classification; Synthetic Aperture Radar;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International

Conference_Location

Quebec City, QC

Type

conf

DOI

10.1109/IGARSS.2014.6946649

Filename

6946649

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=142956