Anomaly and Change Detection in Remote Sensing Images
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Anomaly and Change Detection in Remote Sensing Images

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Anomaly and Change Detection in Remote Sensing Images

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dc.contributor.advisor Camps-Valls, Gustau
dc.contributor.advisor Laparra, Valero
dc.contributor.author Padrón Hidalgo, José Antonio
dc.contributor.other Departament d'Enginyeria Electrònica es_ES
dc.date.accessioned 2021-07-21T09:46:40Z
dc.date.available 2021-07-22T04:45:05Z
dc.date.issued 2021 es_ES
dc.date.submitted 20-07-2021 es_ES
dc.identifier.uri https://hdl.handle.net/10550/79994
dc.description.abstract Earth observation through satellite sensors, models and in situ measurements provides a way to monitor our planet with unprecedented spatial and temporal resolution. The amount and diversity of the data which is recorded and made available is ever-increasing. This data allows us to perform crop yield prediction, track land-use change such as deforestation, monitor and respond to natural disasters and predict and mitigate climate change. The last two decades have seen a large increase in the application of machine learning algorithms in Earth observation in order to make efficient use of the growing data-stream. Machine learning algorithms, however, are typically model agnostic and too flexible and so end up not respecting fundamental laws of physics. On the other hand there has, in recent years, been an increase in research attempting to embed physics knowledge in machine learning algorithms in order to obtain interpretable and physically meaningful solutions. The main objective of this thesis is to explore different ways of encoding physical knowledge to provide machine learning methods tailored for specific problems in remote sensing.Ways of expressing expert knowledge about the relevant physical systems in remote sensing abound, ranging from simple relations between reflectance indices and biophysical parameters to complex models that compute the radiative transfer of electromagnetic radiation through our atmosphere, and differential equations that explain the dynamics of key parameters. This thesis focuses on inversion problems, emulation of radiative transfer models, and incorporation of the above-mentioned domain knowledge in machine learning algorithms for remote sensing applications. We explore new methods that can optimally model simulated and in-situ data jointly, incorporate differential equations in machine learning algorithms, handle more complex inversion problems and large-scale data, obtain accurate and computationally efficient emulators that are consistent with physical models, and that efficiently perform approximate Bayesian inversion over radiative transfer models. es_ES
dc.format.extent 169 p. es_ES
dc.language.iso en es_ES
dc.subject remote sensing es_ES
dc.subject machine learning es_ES
dc.subject change detection es_ES
dc.subject anomaly detection es_ES
dc.title Anomaly and Change Detection in Remote Sensing Images es_ES
dc.type info:eu-repo/semantics/doctoralThesis es_ES
dc.subject.unesco UNESCO::CIENCIAS TECNOLÓGICAS es_ES
dc.embargo.terms 0 days es_ES

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