Pixel Based Sar Image Classification using Random Forest Algorithm
Battula Balnarsaiah1, T.S. Prasad2, P. Laxminarayana3

1Battula Balnarsaiah, Research and Training Unit for Navigational Electronics (NERTU), Department of Engineering and Communication Engineeringand University College of Engineering, Osmania University, Hyderabad, Telangana, India.
2T.S. Prasad, National Remote Sensing Centre (NRSC), Indian Space Research Organization (ISRO), Hyderabad, Telangana, India.
3P. Laxminarayana, Research and Training Unit for Navigational Electronics (NERTU), Osmania University, Hyderabad, Telangana, India.

Manuscript received on 07 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript published on 30 August 2019 | PP: 4351-4356 | Volume-8 Issue-10, August 2019 | Retrieval Number: J98730881019/2019©BEIESP | DOI: 10.35940/ijitee.J9873.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Synthetic Aperture Radar (SAR) images (Microwave data) were classified using Multi-Layer Feed Forward, Cascade Forward Neural Networks and Random Forest (RF) algorithms. For the Random Forest, a general model for classification of Remotely Sensed Radar dual-polarization data based on RF is implemented and classified of SAR image (microwave data) classifications. The RF model exploits spatial context between neighbouring pixels in an image, and temporal class dependencies between different images of the same region, in the case of multi-temporal data. Based on the well-founded experimental on basis of random forest techniques for classification tasks and the encouraging experimental results in RF algorithm , the authors conclude that the proposed RF algorithm is useful for classification of SAR (Sentinel 1A) imagery and evaluate its accuracy and kappa coefficient.
Keywords: Cascade feed forward neural network, Multilayer Feed forward, Random Forest, Synthetic Aperture Radar (SAR) Image Classifications.

Scope of the Article: Classification