|Title||Mixture of Latent Variable Models for Remotely Sensed Image Processing|
|Year of Publication||2014|
|Academic Department||Department of Geography and Environmental Management|
|Thesis Type||PhD. thesis|
The processing of remotely sensed data is innately an inverse problem where properties of spatial processes are inferred from the observations based on a generative model. Meaningful data inversion relies on well-defined generative models that capture key factors in the relationship between the underlying physical process and the measurements.
Mixture of Latent Variable Models for Remotely Sensed Image Processing