Probabilistic surface change detection and measurement from digital aerial ster...

• Autor(es): Jalobeanu, André ; Gama, Cristina ; Gonçalves, José
• Data: 2010
• Identificador Persistente: http://hdl.handle.net/10174/5404
• Origem: Repositório Científico da Universidade de Évora
• Assunto(s): probabilistic inference; quantitative error estimates; Bayesian inference; terrain topography
• Tipo de Documento: Palestra
• Idioma: Português

Descrição

We propose a new method to measure changes in terrain topography from two optical stereo image pairs acquired at different dates. The main novelty is in the ability of computing the spatial distribution of uncertainty, thanks to stochastic modeling and probabilistic inference. Thus, scientists will have access to quantitative error estimates of local surface variation, so they can check the statistical significance of elevation changes, and make, where changes have occurred, consistent measurements of volume or shape evolution. The main application area is geomorphology, as the method can help study phenomena such as coastal cliff erosion, sand dune displacement and various transport mechanisms through the computation of volume changes. It can also help measure vegetation growth, and virtually any kind of evolution of the surface. We first start by inferring a dense disparity map from two images, assuming a known viewing geometry. The images are accurately rectified in order to constrain the deformation on one of the axes, so we only have to infer a one-dimensional parameter field. The probabilistic approach provides a rigorous framework for parameter estimation and error computation, so all the disparities are described as random variables. We define a generative model for both images given all model variables. It mainly consists of warping the scene using B-Splines, and defining a spatially adaptive stochastic model of the radiometric differences between the two views. The inversion, which is an ill-posed inverse problem, requires regularization, achieved through a smoothness prior model. Bayesian inference allows us to recover disparities as probability distributions. This is done on each stereo pair, then disparity maps are transformed into surface models in a common ground frame in order to perform the comparison. We apply this technique to high resolution digital aerial images of the Portuguese coast to detect cliff erosion and quantify the effects of weathering.