On the automation of gestalt perception in remotely sensed data

Gestalt perception, the laws of seeing, and perceptual grouping is rarely addressed in the con-text of remotely sensed imagery. The paper at hand reviews the corresponding state as well in ma-chine vision as in remote sensing, in particular concerning urban areas. Automatic methods can be separated into three types: 1) knowledge-based inference, which needs machine-readable knowl-edge, 2) automatic learning methods, which require labeled or un-labeled example images, and 3) perceptual grouping along the lines of the laws of seeing, which should be pre-coded and should work on any kind of imagery, but in particular on urban aerial or satellite data. Perceptual group-ing of parts into aggregates is a combinatorial problem. Exhaustive enumeration of all combina-tions is intractable. The paper at hand presents a constant-false-alarm-rate search rationale. An open problem is the choice of the extraction method for the primitive objects to start with. Here super-pixel-segmentation is used.