Visible and thermal infrared are two imaging modalities which are used in a variety of applications. In deep learning we need large datasets to be able to train and optimize the algorithms. In thermal infrared imaging, there is a lack of large datasets. This work proposes a deep learning approach to transform visible light images into thermal infrared images using video sequences with moving objects. We propose to use and optimize a CycleGAN algorithm to transform frames from one spectrum to another by training two generators and two discriminators. The results are promising with impressive qualitative and quantitative results.
Coastline segmentation is the process of separating the coastal and backshore zones on aerial images. With a large world population living close to the coast, monitoring coastline changes is critical. Classical computer vision techniques were used to segment the coastline in high quality grayscale images where the difference between the zones was easy to distinguish. However, these techniques are limited to low resolution images and in areas with similar colors or textures. In this work we propose deep convolutional architectures for coastline segmentation using aerial images. An F1 score above 96% was obtained by the best performing model. The obtained results show that our deep models are capable of automatically and accurately detecting coastlines which will help in speeding-up the coastline localization process in large aerial images and improve the efficiency of monitoring coastal areas.
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