Titlebook: Image Fusion; Theories, Techniques H. B. Mitchell Book 2010 Springer-Verlag Berlin Heidelberg 2010 Change Detection.Image Fusion.Image Segm

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Image Sensorsonmental attribute, such as heat, light, sound, pressure, magnetism or motion. However, to be useful, the sensor must map the value of the property or attribute to a quantitative measurement in a . and . manner. To make our discussion more concrete we shall limit ourselves to digital image sensors or cameras.

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Radiometric Calibrationd at different illuminations, or under different atmospheric conditions or captured by different sensors. Radiometric calibration is used in both image fusion and in feature map fusion. For the sake of concreteness, we shall concentrate on the radiometric calibration of two input images . and ..

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Image Sub-space Techniquesis constructed from the input images. The techniques considered in this chapter include: principal component analysis (PCA), non-negative matrix factorization (NMF), canonical correlation analysis (CCA) and linear discriminant analysis (LDA).

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Markov Random Fieldsls and correlated features. Contextual models are one way to model prior information and MRF theory can be applied to model a prior probability of contextual dependent patterns. Maximum . (MAP) probability is one of the most popular criteria for optimality and widely applied for MRF modeling.

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Introductionwith . information is best handled within a statistical framework. In particular we shall restrict ourselves to classical and robust statistical approaches, Bayesian methods, sub-space and wavelet techniques.

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Spatial Alignment In order to keep our discussion focused we shall concentrate on the image registration of two input images, . and ., which we define as finding the transformation . which “optimally” maps spatial locations in the image . to the corresponding spatial locations in the image ..

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Semantic Equivalenceaturemaps rarely refer to the same object or phenomena. In this case, fusion can only take place if the features maps are semantically equivalent. This is also true in the case of decision map fusion. In this chapter we shall therefore concentrate on the semantic equivalence of feature maps and decision maps.

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