1 research outputs found
Deep Learning Based Classification Techniques for Hyperspectral Images in Real Time
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