Bachelor and Master Theses Propositions in Remote Sensing

Focus Areas

  • Land use and land cover classification
  • Pattern recognition in large data sets
  • Classification methods, such as supervised learning, unsupervised learning, self-taught learning
  • Feature learning / representation learning
  • Analysis techniques, such as clustering or archetypal analysis
Probabilistic classifiers for detection of unknown classes in multispectral remote sensing data

German title: Probabilistische Klassifikation für die Detektion unbekannter Klassen in multispektralen Fernerkundungsdaten

Classification is one of the most informative means for interpreting remote sensing images. One challenge is the interpretation of areas with unknown land use and land cover classes, i.e. they are not included in the classification model. In order to ensure a reliable and certain classification result, these areas need to be identified by means of suitable techniques. Probabilistic classifiers with reconstructive model component are able to detect these areas. The goal of the thesis is the assessment of the suitability of different probabilistic classifiers for remote sensing data classification. The evaluation will be conducted on various multispectral datasets with differing characteristics.

Supervision: Ribana Roscher, Susanne Wenzel

Sparse Representation for root segmentation in hyperspectral images

root image in rhizotrone

German title: Sparse Representation für die Segmentierung von Wurzeln in Hyperspecktralbildern

The segmentation of roots in images is challenging due to their similar color appearance and their thin structure. Sparse representation can be used for segmentation by representing each image patch by a weighted linear combination of basis patches (e.g., [2]). These basis patches are learned from annotated images and will be collected in discriminative dictionaries, which contain the most distinct structures of roots and background. The suitability of the approach will be evaluated by means of hyperspectral images of plants grown in flat rhizotrons [1].

[1] Steier, A., Cendrero-Mateo, P., Malolepszy, T., Gioia, T., Briese, C., & Rascher, U. (2013). HIDeR – Hyperspectral Imaging Device for Rhizotrons Simultaneously Scanning of Shoot and Root in Plant Phenotyping, DPPN.
[2] Berkels, B., Kotowski, M., Rumpf, M., & Schaller, C. (2011, May). Sulci detection in photos of the human cortex based on learned discriminative dictionaries. In International Conference on Scale Space and Variational Methods in Computer Vision, 326-337.

Supervision: Ribana Roscher, Uwe Rascher

Incremental learning on large image mosaics

German title: Inkrementelles Lernen auf großen Bildverbänden

Large image mosaics from remote sensing images are characterized by spatial and temporal differences, and thus spectral differences. Therefore, the interpretation of large image mosaics is challenging. Especially if the number of labeled samples with information of land use and land cover is limited, the learning of a well generalized classification model is difficult. In contrast to batch-based learning methods, incremental learners are able to train a classification model which adapts to the given data over time. Goal of this thesis is the classification of an image mosaic using incremental learning. A main part of the thesis will consider the acquisition of new data samples in order to adapt the classification model to current conditions.

Supervision: Ribana Roscher, Susanne Wenzel

Linking people and pixel – patterns of forest cover change

German title: Verbindung von Mensch und Pixel – Muster von Entwaldungsprozessen

Primary forests and forests influences by natural or man made causes can show substantially different structures. Automatically identifying such structures can be a prerequisite pre-processing step for land cover and land use classification. Goal of this thesis is to find patterns in forest cover change using archetypal analysis [1]. This technique is able to find extreme patterns, so-called archetypes. Determined archetypes should be visually evaluated and compared to results obtained from clustering.
[1] Cutler, A., & Breiman, L. (1994). Archetypal analysis. Technometrics, 36(4), 338- 347.

Supervision: Ribana Roscher, Sven Lautenbach