Christian Knoth - Introduction to Deep Learning in R for analysis of UAV-based remote sensing data

Описание: Summary: The aim of this tutorial is to develop a basic understanding of the key practical steps involved in creating and applying a convolutional neural network (CNN) for image analysis – and how to do that in R.
These steps are:
Building your model
Preparing your data
Training your model
Predicting with your model
Besides the basic workflow, we will discuss two strategies for tackling small data problems, which is specifically important when working with UAV-based data: data augmentation and transfer learning.
In addition, we will look at aspects that are important for many remote sensing applications of CNNs: we´ll develop a model for pixel-by-pixel classification (instead of image classification) using an architecture called “U-net”. We will also address the practical question of how to turn a remote sensing image into something that can be processed by our CNN, and how to reassemble the predictions back to a map.
Finally, we will briefly touch on the topic of inspecting what a trained model has learned.

Installation instructions & material: https://github.com/DaChro/ogh_summer_...

References:
Chollet, F., and J.J. Allaire. 2018. Deep Learning with R. Manning Publications.

Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” In Medical Image Computing and Computer-Assisted Intervention – Miccai 2015

How to cite this video:
http://doi.org/10.5446/49550