Remote sensing technology in recent years has been regarded the most important source to provide substantial information for delineating the flooding extent to the disaster management authority. There have been numerous studies proposing mathematical or statistical classification models for flood mapping. However, conventional pixel-wise classifications methods rely on the exact match of the spectral signature to label the target pixel. In this study, we propose a fully convolutional neural networks (F-CNNs) classification model to map the flooding extent from Landsat satellite images. We utilised the spatial information from the neighbouring area of target pixel in classification. A total of 64 different models were generated and trained with a variable neighbourhood size of training samples and number of learnable filters. The training results revealed that the model trained with 3 × 3 neighbourhood sized training samples and with 32 convolutional filters achieved the best performance out of the experiments. A new set of different Landsat images covering flooded areas across Australia were used to evaluate the classification performance of the model. A comparison of our proposed classification model to the conventional support vector machines (SVM) classification model shows that the F-CNNs model was able to detect flooded areas more efficiently than the SVM classification model. For example, the F-CNNs model achieved a maximum precision rate (true positives) of 76.7% compared to 45.27% for SVM classification.
An unmanned aerial vehicle (UAV), also known as a drone, refers to a pilotless aircraft, a flying machine without an onboard human pilot or passengers. As such, 'unmanned' implies the total absence of a human who directs and actively pilots the aircraft. Control functions for unmanned aircraft may be either onboard or off-board (remote control). That is why the terms remotely operated aircraft (ROA) and remotely piloted vehicle (RPV) are in common use as well [1]. The term UAV has been used for several years to describe unmanned aerial systems. Various definitions have been proposed for this term, like [2]:A reusable 1 aircraft designed to operate without an onboard pilot. It does not carry passengers and can be either remotely piloted or pre-programmed to fly autonomously.Recently, the most reputable international organizations -like the International Civil Aviation Organization (ICAO), EUROCONTROL, the European Aviation Safety Agency (EASA), the Federal Aviation Administration (FAA) -as well as the US Department of Defense (DoD), adopted unmanned aircraft system (UAS) as the correct official term. The changes in acronym are caused by the following aspects:1 The characterization reusable is used to differentiate unmanned aircraft from guided weapons and other munitions delivery systems.
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