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Land Use Patch Generalization Based on Semantic Priority
- Source :
- Abstract and Applied Analysis, Vol 2013 (2013), Abstr. Appl. Anal.
- Publication Year :
- 2013
- Publisher :
- Hindawi Limited, 2013.
-
Abstract
- Land use patch generalization is the key technology to achieve multiscale representation. We research patches and achieve the following. (1) We establish a neighborhood analysis model by taking semantic similarity between features as the prerequisite and accounting for spatial topological relationships, retrieve the most neighboring patches of a feature using the model for data combination, and thus guarantee the area of various land types in patch combination. (2) We establish patch features using nodes at the intersection of separate feature buffers to fill the bridge area to achieve feature aggregation and effectively control nonbridge area deformation during feature aggregation. (3) We simplify the narrow zones by dividing them from the adjacent feature buffer area and then amalgamating them into the surrounding features. This effectively deletes narrow features and meets the area requirements, better generalizes land use features, and guarantees simple and attractive maps with appropriate loads. (4) We simplify the feature sidelines using the Douglas-Peucker algorithm to effectively eliminate nodes having little impact on overall shapes and characteristics. Here, we discuss the model and algorithm process in detail and provide experimental results of the actual data.
- Subjects :
- Article Subject
Generalization
Intersection (set theory)
Applied Mathematics
lcsh:Mathematics
Process (computing)
computer.software_genre
lcsh:QA1-939
Semantic similarity
Simple (abstract algebra)
Feature (computer vision)
Key (cryptography)
Data mining
Representation (mathematics)
computer
Analysis
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 16870409 and 10853375
- Volume :
- 2013
- Database :
- OpenAIRE
- Journal :
- Abstract and Applied Analysis
- Accession number :
- edsair.doi.dedup.....e51c422384713864cc1f0fab05326f2d