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Self-organizing maps and Gestalt organization as components of an advanced system for remotely sensed data: An example with thermal hyper-spectra

: Michaelsen, Eckart


Pattern recognition letters 83 (2016), Pt.2, pp.169-177
ISSN: 0167-8655
Journal Article
Fraunhofer IOSB ()
hyperspectral data; thermal spectra; self-organizing maps; unsupervised learning; Gestalt grouping

The thermal hyper-spectral data provided for research by IEEE-GRS/Telops in 2014 give an interesting example for combining such spectral information with perceptual grouping according to Gestalt laws in the geographic plane. Self-organizing maps are used for unsupervised learning. By watershed segmentation and subsequent merging on the map certain classes are found automatically. Back-projection of these regions to the geographic plane reveals considerable coincidence with classes of human interest such as vegetation, building roofs, and roads, respectively. The partial ground-truth provided with the data by IEEE-GRS/Telops allows the estimation of quantitative recognition accuracies. Human observers assign such meaning to the back-projected segments relying mainly on their perceptual grouping capabilities – roads appear as elongated stripes organized in a net, buildings come as blobs in organized patterns of repetitive rows and mirror-symmetry, and subsequently the rest is inferred as probably being vegetated. The automatic Gestalt grouping presented in this work follows the rules of Gestalt algebra. Gestalt hierarchies of depth three can be instantiated on the building class in accordance with human perception. Interesting feed-back possibilities are proposed from the perceptual grouping to the interpretation of the segments on the self-organizing map and further on to the assignment of meaning to the spectra. Again the ground-truth is used to estimate the gain quantitatively.