Abstract
Abstract Unmanned aerial vehicles (UAV) can be used as basic elements of the sensor network or an upgrade of existing network that are built with static wireless sensor nodes. Wireless Networks (WN) are utilized across in surveillance in all domains like natural disasters, agriculture, water, forest, military, buildings, health monitoring, disaster relief & emergency management, area and industrial surveillance, due to its wider applicability. Software Defined Networking (SDN) provide a hopeful resolution in bendy supervision WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based supervision in structure of WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. Synthetic Aperture Radar (SAR) images are difficult to analyze due to the presence of speckle noise. Speckle noise must be filtered out before applying to other image processing applications. Three Layered Feed Forward Back Propagation Neural Network (TLFFBPNN) has been proposed to suppress the speckle noise. GLCM properties have been extracted and Back propagation training algorithm is used to train the neural network.
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