Master of science Sara Maria Björk will Friday October 6th, 2023, at 12:15 hold her disputas for the PhD degree in Science. The title of her thesis is:
"Deep convolutional regression modelling for forest parameter retrieval"
Accurate forest monitoring is crucial as forests are major global carbon sinks. Additionally, accurate prediction of forest parameters, such as forest biomass and stem volume (SV), has economic importance. Therefore, the development of regression models for forest parameter retrieval is essential.
Existing forest parameter estimation methods use regression models that establish pixel-wise relationships between ground reference data and corresponding pixels in remote sensing (RS) images. However, these models often overlook spatial contextual relationships among neighbouring pixels, limiting the potential for improved forest monitoring. The emergence of deep convolutional neural networks (CNNs) provides opportunities for enhanced forest parameter retrieval through their convolutional filters that allow for contextual modelling. However, utilising deep CNNs for regression presents its challenges. One significant challenge is that the training of CNNs typically requires continuous data layers for both predictor and response variables. While RS data is continuous, the ground reference data is sparse and scattered across large areas due to the challenges and costs associated with in situ data collection.
This thesis tackles challenges related to using CNNs for regression by introducing novel deep learning-based solutions across diverse forest types and parameters. To address the sparsity of available reference data, RS-derived prediction maps can be used as auxiliary data to train the CNN-based regression models. This is addressed through two different approaches.
The Disputas will be led by Professor Arne Smalås, Dean of the Faculty of Science and Technology, UiT.
The disputas and trial lecture will be streamed from these sites:
Disputas (12:15 - 16:00)
Trial Lecture (10:15 - 11:00)
The thesis is available through Munin.