Computer-Aided Mapping of Hyper- and Multi-Spectral Data

• Applicants: Professor Dr.-Ing. Gernot A. Fink; Professor Dr. Christian Wöhler
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2015 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 269661170

Computer aided mapping of multi-spectral and hyper-spectral data is an important application in planetary science. The light reflected from a planetary surface is acquired in many channels across a broad range of wavelengths. This allows for analyzing physical and geological surface properties, e.g., in order to determine the occurrence of certain minerals or rocks or to explore planets for future space missions. For this purpose, large databases of multi-spectral and hyper-spectral images are commonly analyzed by experts mostly in a manual fashion. The objective of this project is to support such analyses with automated machine learning methods. A major challenge lies in the lack of annotated training material which is required in order to estimate classifiers from sample data. For this reason, it is proposed in the project to follow an active learning strategy. Image regions are clustered in an unsupervised manner before individual annotations are requested from the expert. The annotations correspond to prototypical regions and can be propagated to similar and so far unknown regions. Based on the increasing amount of annotated samples, deep neural networks are trained in order to automate the mapping process further. Modeling the uncertainty of the automated decisions is one of the most important aspects in the project. The capabilities of the machine learning methods become transparent and improve the interpretability of the results for the expert. Another very important aspect is the representation of image regions. Instead of an assignment to only a single class in the classification process, regions are represented in terms of attributes which characterize a region with respect to selected properties. Classes of regions are recognized by their attributes. This even allows for recognizing classes which are unseen in the training data set. The methods will be evaluated on multi-spectral and hyper-spectral images as well as semantic segmentation benchmarks considered in the computer vision community. A qualitative analysis is performed by a planetary geologist who evaluates the support provided by the methods in the exploration of a new data set.

• DFG Programme: Research Grants