Object-Based and Rule-Based Classification of Synthetic Aperture Radar Images
Aishwarya Rastogi1, Amit Doegar2
1Aishwarya Rastogi*, Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India.
2Amit Doegar, Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2458-2463 | Volume-8 Issue-10, August 2019 | Retrieval Number: J95340881019/2019©BEIESP | DOI: 10.35940/ijitee.J9534.0881019
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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: NSince thousands of years, the land is the basic and very important requirement for humans to survive and grow. The surface area of the earth provided by nature contains many different geographical locations divided into oceans, mountains, rivers, barren land, fertile land, ice caps and many more. The huge land masses and water bodies need to be observed and analyzed for optimum utilization of resources. Remote sensing is the best possible way to observe the earth’s surface from a distance through different satellites and sensors. But most of the satellite images are not clear up to the extent to classify different terrain features accurately. Hence classification of image is needed to observe different terrain features in original images. In this study, the aim is to propose a branch of natural computation for SAR image classification into different terrain features with better information retrieval and accuracy measures as compared to traditional methods for satellite image classification. The object-based analysis has been used to extract spectral reflectance of five texture measures namely urban, rocky, vegetation, water and barren to generate training set. Minimum distance to mean classifier has been used with one of the Nature Inspired computation technique i.e. bacterial foraging optimization algorithm for the satellite image classification, to extract the more accurate information about land area of Alwar district, Rajasthan, India. In the proposed study a high-quality thematic map has been generated with the 7-band multi-spectral, medium-resolution satellite images. This approach provides the greater speed and accuracy in its computation with 97.43% overall accuracy (OA) and 0.96 Kappa co-efficient.
Keywords: Synthetic Aperture Radar (SAR) Image, Land use land cover (LULC), Object-based image analysis (OBIA), Bacterial Foraging Optimization Algorithm (BFOA)
Scope of the Article: Classification