Browsing by Subject "UMBC Computer Vision and Remote Sensing Laboratory"
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Item Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment(2020-09-02) Rahnemoonfar, Maryam; Chowdhury, Tashnim; Murphy, Robin; Fernandes, OdairIn this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic segmentation. The dataset consists of around 2000 high-resolution aerial images, with annotated ground-truth data for semantic segmentation. We discuss the challenges of the dataset and train the state-of-the-art methods on this dataset to evaluate how well these methods can recognize the disaster situations. Finally, we discuss challenges for future research.Item Deep multi-scale learning for automatic tracking of internal layers of ice in radar data(Cambridge University Press, 2020-10-12) Rahnemoonfar, Maryam; Yari, Masoud; Paden, John; Koenig, Lora; Ibikunle, OluwanisolaIn this study, our goal is to track internal ice layers on the Snow Radar data collected by NASA Operation IceBridge. We examine the application of deep learning methods on radar data gathered from polar regions. Artificial intelligence techniques have displayed impressive success in many practical fields. Deep neural networks owe their success to the availability of massive labeled data. However, in many real-world problems, even when a large dataset is available, deep learning methods have shown less success, due to causes such as lack of a large labeled dataset, presence of noise in the data or missing data. In our radar data, the presence of noise is one of the main obstacles in utilizing popular deep learning methods such as transfer learning. Our experiments show that if the neural network is trained to detect contours of objects in electrooptical imagery, it can only track a low percentage of contours in radar data. Fine-tuning and further training do not provide any better results. However, we show that selecting the right model and training it on the radar imagery from the start yields far better results.