Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 3 OBJECT AND IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: BAYESIAN METHODS: APPLICATIONS IN INFORMATION AGGREGATION AND IMAGE DATA MINING. Mihai Datcu and Klaus Seidel

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 Fig. 3. Paradigm of Bayesian information and knowledge fusion. models Mj and M 2 . The paradigm of Bayesian information and knowledge fusion is presented in Fig. 3. In the second paradigm, we address mainly the fusion of information for the physical characterization of scenes, e.g. estimation of terrain heights derived jointly from image intensities and SAR interferometric phases (Nicco et al., 1998). corresponding neighbourhood, Z partition function and T temperature (Datcu et al., 1998; Geman and Geman, 1984; Schroder et al., 1998). Images and other multidimensional signals satisfy the local requirement that neighbouring sites have related intensities. On the other hand, a model should also be able to represent long- range interactions. This is a global requirement. Gibbs random fields are able to satisfy both requirements. The equivalence of Gibbs and Markov random fields gives the appropriate mathematical techniques to treat these behaviours. A pragmatic problem is to fit optimally a Gibbs random field model to real data. It was expected that a maximum likelihood estimate gives the desired result. That is not generally possible due to the requirement to evaluate the partition function. Several alternative solutions have been proposed: the coding, and the maximum pseudo-likelihood. However, none of these is efficient. Recently, a consistent solution for the maximum likelihood was introduced: Monte Carlo maximum likelihood. This algorithm requires no direct evaluation of the partition function, it is consistent and converges to the maximum likelihood with probability one (Cowles and Carlin, 1996; Li, 1995). 3. STOCHASTIC MODELS A “universal” model for stochastic processes is not mathematically tractable. In applications, we are faced either with simple case studies, e.g. the data is precisely described by a low complexity stochastic model, as in laser speckle images, or the data is of high complexity, and then we use an approximate model. A challenging research task is to find a “quasi-complete” family of models for a certain class of signals, for example all images provided by one sensor. In this spirit, we concentrate on the following stochastic models: Gibbs random fields, multidimensional stochastic processes, cluster based probability modelling. 3.1. Gibbs random fields In many situations signals satisfy predetermined constraints. We can restrict the modelling of these signals by considering only probability measures that fulfil these constraints. Here, we have to choose the appropriate probability measure: the one satisfying the set of constraints. Applying a maximum uncertainty principle, the probability measure that satisfies all relevant constraints should be the one that maximizes our incertitude about what is unknown to us. The probability measure p(x) resulting from such a principle is a Gibbs distribution: 1 J f HM P(x) = 2 e Z — ■I HM = I v clique^*» allcliques 9 = {a 0 ,a 1 ,...a 4 ,ß 1 ,...ß 4 ,Y 0 } (7) where cxq, ...a 4 , (3 1 ,...(3 4 , y 0 are the model parameters associated to the corresponding cliques, V is the potential function characterizing the interaction between the samples of the random field inside the clique, H represents the energy function for the 3.2. Cluster based probability models Non-parametric modelling allows more flexibility, but results in more complex algorithms and requires large training datasets. From the class of non-parametric models, the kernel estimate plays the last period an important role in signal processing. Kernel estimation works with the hypothesis of smooth probability density functions using a generalization of the training dataset. N HX) = ¡j X k(x ~ x „> (*> n = 1 where p is the probability density function, k a kernel, X is the data vector. The kernel k(X) radiates probability from each vector in the learning sample and N is the number of kernels. The learning sample is generalized. A combined technique that uses the generalization property of kernel estimation and the summarizing behaviour of cluster analysis was proposed. The technique requires clustering of the training data and fitting separable Gaussians to each of the resulting regions. N d **>= 5> m n>«,№ <*> m = 1 i = 1 where w the measure of number of points in one cluster and d the number of centers of action. Cluster based estimation first finds the centres of action (clustering). It uses a single kernel in one cell. This method is successful for treating high dimensional data, is able to capture high-order non-linear relationships, can be applied in a multiscale algorithm, and shows a good representation of the tails of the distribution (Popat and Picard, 1997). 3.3. Stochastic pyramids The wavelet transformation of images in its operator formalism suggests the decomposition of the signal into two components:

Access restriction

Copyright

fullscreen: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Monograph

Chapter

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation