Reliable Detection of Water Areas in Multispectral Drone Imagery : A faster region-based CNN model for accurately identifying the location of small-scale standing water bodies

Abstract: Dengue and Zika are two arboviral viruses that affect a significant portion of the world population. The principal vector species of both viruses are Aedes aegypti and Aedes albopictus mosquitoes. They breed in very slow flowing or standing pools of water. It is important to reduce and control such potential breeding grounds to contain the spread of these diseases. This thesis investigates a model for the detection of water bodies using high-resolution images collected by Unmanned Aerial Vehicles (UAVs) in tropical countries, exemplified by Sri Lanka, and their multispectral information to help detect water bodies where larvae are most likely to breed quickly and accurately. Although machine learning has been studied in previous work to process multispectral image information to obtain the location of water bodies, different machine learning methods have not been compared, only random forest algorithms have been used. Because Convolutional Neural Networks (CNNs) are known to provide advanced classification performance for visual recognition tasks, in this thesis, faster region-based CNNs are introduced to perform fast and accurate identification of water body locations. In order to better evaluate the experimental results, this thesis introduces Intersection over Union (IoU) as a criterion for evaluating the results. On the one hand, IoU can judge the success rate of the model for water region recognition, and on the other hand, analysis of the model recall rate under different IoU values can also evaluate the model’s ability to detect the range of water regions. Meanwhile, the basic CNN network and random forest algorithm in the previous work are also implemented to compare the results of faster region-based CNNs. In conclusion, the faster region-based CNN model achieves the best results with a 98.33% recognition success rate for water bodies in multispectral images, compared to 95.80% for the CNN model and 95.74% for the random forest model. In addition, the faster region-based CNN model significantly outperformed the CNN model and the random forest model for training speed.