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An efficient computational framework for labeling large scale spatiotemporal remote sensing datasets
- Source :
- IC3
- Publication Year :
- 2014
- Publisher :
- IEEE, 2014.
-
Abstract
- We present a novel framework for semisupervised labeling of regions in remote sensing image datasets. Our approach works by decomposing the image into irregular patches or superpixels and derives novel features based on intensity histograms, geometry, corner density, and scale of tessellation. Our classification pipeline uses either k-nearest neighbors or SVM to obtain a preliminary classification which is then refined using Laplacian propagation algorithm. Our approach is easily parallelizable and fast despite the high volume of data involved. Results are presented which showcase the accuracy as well as different stages of our pipeline.
- Subjects :
- Tessellation (computer graphics)
Parallelizable manifold
Scale (ratio)
Computer science
business.industry
Pipeline (computing)
Feature extraction
Pattern recognition
Image (mathematics)
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Computer Science::Computer Vision and Pattern Recognition
Histogram
Artificial intelligence
business
Remote sensing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2014 Seventh International Conference on Contemporary Computing (IC3)
- Accession number :
- edsair.doi...........58681ce6499eab74281a89f9d7a9a3fa