Section: Computer Science and Engineering
Tutor: AMIGONI FRANCESCO
Advisor: COMAI SARA Major Research topic
:Advances in Deep Learning Techniques for Land Cover Classification of Remote Sensing DataAbstract:
The advances in remote sensing technologies accompanied by the extraordinary developments in hardware and software capabilities has leveraged the earth data collection, distribution, and analysis for a better capitalization of the latest AI techniques. The massive amount of high resolution remotely sensed data acquired with a temporal frequency from the entire earth is apparently a rich source of information about the earth states and its continuous changes. However, the full exploitation of data and the automatic extraction of such information is still a challenge.
Land use and land cover are the core knowledge about the earth surface for any earth monitoring, development, controlling and planning task. On the other hand, collection of such information on a temporal basis from extensive ground assessments is very expensive and challenging. Therefore, the focus of most research works aiming at employing AI on remotely sensed data is to reach an automatic land cover/use classification with the highest accuracy.
The development of brain-inspired deep learning techniques during the past decade has shifted all the expectations from the AI into a new level. Being beyond the human performance, deep learning techniques have proven the incredible capability of machine in understanding and analysis of complex big volume data. Multispectral images acquired by the optical remote sensors are intrinsically complex and rarely used by machine learning techniques in research. The format of such data passes all the requirements to employ deep learning techniques, with possibly some customization and data prepossessing. Moreover, the rapid advances in optical sensors and expansion of spectral channels with rich information about the earth surface, is an absolute evidence of how important the exploration of such data is.
This PhD work focuses on the potentials of Deep Learning in order to classify and extract new information from the available complex unprecedented global big set of Hyperspectral images that are taken from the earth surface by the airborne and spaceborne sensors. Hypersepctral imagery data, with hundreds of spectral bands, is not only a unique type of data that introduces the image multi-dimensional feature spaces, but it also offers entirely new opportunities for variety of applications in industries, business and also government-related sectors. Deep learning with its promising capability in classifying Big Complex Data is a great candidate for land cover classification of Hyperspectral images. This research work collects the state-of-the-art, explores the available challenges, and discusses solutions for a practical land-cover classification of hyperspectral imagery datasets.