| Title | Semi-supervised map regionalization for categorical data |
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| Author | Beauchemin, M |
| Source | International Journal of Remote Sensing 2019 p. 1-11, https://doi.org/10.1080/2150704X.2019.1633485 |
| Image |  |
| Year | 2019 |
| Alt Series | Natural Resources Canada, Contribution Series 20180171 |
| Publisher | Informa UK Limited |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf (Adobe® Reader®); html |
| Subjects | geophysics; Science and Technology; remote sensing; mapping techniques; cartography; statistical methods; Methodology; Classification |
| Illustrations | flow diagrams; sketch maps |
| Program | Remote Sensing Science |
| Released | 2019 07 10 |
| Abstract | The objective of map regionalization is to group contiguous objects on a map into larger entities sharing similar properties or relationships, resulting in homogeneous regions that are easier to
interpret. We propose a strategy to interactively incorporate human perception of homogeneous regions to improve unsupervised regionalization processes. The approach fits within the well-known segmentation/clustering framework. The method operates on
a categorical map, introduces a contour detector for boundaries delineation with better resolution power than a regular grid tessellation to initiate a region growing process, and integrates the role of a human analyst for better classification of
homogeneous areas through a semi-supervised clustering (SSC) method. This last step is achieved using pairwise clustering constraints on regions identified by the analyst on the monitor. The potential of the proposed strategy is illustrated with data
extracted from the Earth Observation for the sustainable development of forests (EOSD) map of Canada. Comparisons with a recently introduced algorithm for map regionalization are provided for three different spatial scales at different steps of the
method. |
| Summary | (Plain Language Summary, not published)
The objective of map regionalization is to group contiguous objects on a map into larger entities sharing similar properties or relationships, resulting
in homogeneous regions easier to interpret. In this communication, we propose an algorithm to perform map regionalization with limited user supervision. The potential of the method is illustrated with an example. |
| GEOSCAN ID | 308490 |
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