Abstract
The detection and evaluation of flood damage in rural zones are of great importance for farmers, local authorities, and insurance companies. To this end, the paper proposes an efficient system based on five neural networks to assess the degree of flooding and the remaining vegetation. After a previous analysis the following neural networks were selected as primary classifiers: you only look once network (YOLO), generative adversarial network (GAN), AlexNet, LeNet, and residual network (ResNet). Their outputs were connected in a decision fusion scheme, as a new convolutional layer, considering two sets of components: (a) the weights, corresponding to the proven accuracy of the primary neural networks in the validation phase, and (b) the probabilities generated by the neural networks as primary classification results in the operational (testing) phase. Thus, a subjective behavior (individual interpretation of single neural networks) was transformed into a more objective behavior (interpretation based on fusion of information). The images, difficult to be segmented, were obtained from an unmanned aerial vehicle photogrammetry flight after a moderate flood in a rural region of Romania and make up our database. For segmentation and evaluation of the flooded zones and vegetation, the images were first decomposed in patches and, after classification the resulting marked patches were re-composed in segmented images. From the performance analysis point of view, better results were obtained with the proposed system than the neural networks taken separately and with respect to some works from the references.
Highlights
Detection and segmentation of small regions of interest (RoIs) from images is a difficult task in many remote image processing applications
We considered that a solution to improve the accuracy and the time performances is to combine several classifiers that act in a parallel way and to aggregate individual decisions to largely eliminate classification errors
The image processing for the purpose of segmentation involves the most appropriate choice of representation. Another problem encountered in remote image processing is the radiometric calibration, but we considered that unmanned aerial vehicles (UAVs) data is usually not calibrated in the same way as images taken from space platforms
Summary
Detection and segmentation of small regions of interest (RoIs) from images (e.g., natural vegetation areas, crops, floods, forests, roads, buildings, waters, etc.) is a difficult task in many remote image processing applications. The utility of the surveillance/monitoring systems on various areas has been proven by the management of natural disasters [7] and rescue activities. Different solutions based on image analysis are proposed for detection and analysis of RoIs in areas affected by different types of natural disasters (floods, hurricanes, tornadoes, volcanic eruptions, earthquakes, tsunamis, etc.). Determining and evaluating flooded areas during or immediately after flooding in agricultural zones are important for timely assessment of economic damage and taking measures to remedy the situation
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