Automation in photogrammetry is largely made possible by stereo-matching techniques that enable (semi-)automatic aero-triangulation and creation of digital elevation models. Matching: I know what it is, I know what it does, but how does it work??

The aim of matching is to identify corresponding phenomena in two or more sets. In photogrammetry the sets are the left and right image of a stereo pair, and the problem in corresponding them is to trace and locate in the right image the conjugate of a point in the left image. Ever since images could be stored in a computer as pixels stereo-matching algorithms have been being developed there are many and new ones emerge on a regular basis. The methods may be categorised into two broad classes depending on how the image is approached. From the signal-processing perspective an image is regarded as a set of grey values or colour representing the intensity of reflected electromagnetic signals. But an image may also be seen as a representation of features present in object space where each feature, such as the corner of a building, a road crossing or tree, is represented by an irregularly shaped group of pixels.

In the signal-based approaches, also called area or intensity-based, correspondence is sought using intensity values in a regularly shaped patch. A target patch the size, for example, of 9×9 pixels, is defined and shifted over a search patch in the other image the size of which depends on how well the approximate location of the conjugate point is known. For each position a similarity measure is computed, for example, the normalised cross-correlation, resulting in a connected set of similarity measurements, one for each pixel. The highest similarity value determines the corresponding patches, their centre pixels selected as corresponding points. Acceptance of the match depends on whether the similarity value exceeds a predefined threshold; the value can be selected in a heuristic way or, when using normalised cross-correlation, on a statistically sound basis using student t-testing. Sub-pixel accuracy can be achieved by fitting a function, for example a second-order polynomial, through the correlation values and then determining the maximum of that function.

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