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Semantic Segmentation Of Polsar Data Using Machine Learning

Land cover and land use analysis aims at mapping and monitoring the geo/biophysical parameters of Earth’s surface, a process critical in the environment and urban sciences studying the ever-changing evolution of our planet. This analysis is very important to address the everincreasing environmental concerns like climate change, deforestation, soil degradation, water pollution, and air pollution. These crucial issues mandate the need for creating highly accurate land cover maps but despite this fact, such endeavors are hindered by the unavailability of remote sensing data and in case remote sensing data is available, the ground truth corresponding to that remote sensing data is not available most of the times which is necessarily required for validation purpose. Moreover, the speckle noise and geometrical distortions make land cover classification a challenging task especially in complex landscapes. To meet all these demanding requirements of global land cover monitoring, high-resolution imaging sensors acquire multispectral, hyperspectral, optical, and radar data. Optical sensors and synthetic aperture radar (SAR) are the most commonly used earth observation instruments abroad satellites. Optical sensors have the advantage of obtaining data from multiple wavelengths. However, the use of an optical sensor can be problematic in some tropical regions due to cloud cover. Whereas in the case of SAR data, cloud cover does not affect data acquired since microwaves used by SAR sensors can penetrate through clouds and the data could be used for characterization of land use and land cover. Also, another advantage of SAR-based remote sensing is its capability to capture data day and night which enables the capture of long time series datasets that can be used to monitor land cover changes.

Rajat Garg, Assistant Professor,
Department of Mechanical Engineering,
Lloyd Institute of Engineering & Technology, Greater Noida

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