Remote Sensing Image Analysis and Interpretation: Classification with Bayes' theorem

Fourth lecture in the course 'Remote Sensing Image Analysis and Interpretation' covering the topics of Maximum likelihood estimation, maximum a posteriori estimation and how a classifier is evaluated.
slides: uni-bonn.sciebo.de/s/NS4RnVYJ...
Lecturer: Ribana Roscher
Winter term 2020/2021, University of Bonn

[@MrMuhammad2010]

Thanks for sharing. Quite useful.

[@23win]

Hello,
I followed your video on remote sensing basics and image classification in the context of GIS with great interest. I find your explanations particularly clear and instructive.
While watching this video, a question came to mind: do you think it would be possible to find a thesis topic with a physics emphasis that could be applied in the field of GIS, especially in the context of processing images obtained through remote sensing, or even in remote sensing itself?
Although I understand that currently mathematics play a crucial role in many GIS researches, I am convinced that physical principles could also offer interesting perspectives. For example, applying the laws of physics to model light-matter interactions in satellite images, or using advanced physical concepts to improve image classification algorithms. The integration of physical models into geographic information systems for a better understanding of environmental phenomena, or even the use of concepts from quantum physics (qbits - cub sats .....)/statistical physics for innovative applications in the field of geomatics?
I would be very interested in your thoughts on this topic. Do you think there are opportunities to further explore this intersection between physics and GIS in the context of academic research, with possible opportunities to conduct experiments in a laboratory, for example, to improve acquisition methods (such as LIDAR)?
Thank you again for the time you devote to sharing your knowledge and experience on your TH-cam channel.
Best regards,
AOUINATOU OUSSAMA