PhD defense: 'Deep Learning-based techiniques for real-time classification of Hyperespectral Images'
Remote sensing can be defined as the acquisition of information from a given scene without coming into physical contact with it, through the use of sensors, mainly located on aerial platforms, which capture information in different ranges of the electromagnetic spectrum.
The objective of this thesis is the development of efficient schemes, based on the use of deep learning neural networks, for the classification of remotely sensed multi and hyperspectral land cover images. Efficient schemes are those that are capable of obtaining good results in terms of classification accuracy and that can be computed in a reasonable amount of time depending on the task performed. Regarding computational platforms, multicore architectures and Graphics Processing Units (GPUs) will be considered.