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A bag-of-visual words approach based on optimal segmentation scale for high resolution remote sensing image classification
- Source :
- IGARSS
- Publication Year :
- 2015
- Publisher :
- IEEE, 2015.
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Abstract
- High resolution remote sensing imagery can provide more useful information, such as spectral, shape and texture information. However, traditional pixel-based image classification approaches may suffer the increase of within-class spectral variation with improved spatial resolution. This paper presents a novel method which combines the optimal segmentation scale with Bag-of-Visual Words (BOV) representation for object-oriented classification. More precisely, an improved estimation of scale parameter (ESP) tool is adopted to determine the optimal parameters in multi-scale image segmentation. BOV is introduced to construct the midlevel representations instead of low-level features for object description. Then Support vector machine (SVM) is used for classification. And the experiments are conducted on high spatial resolution images to validate the proposed algorithm.
- Subjects :
- Pixel
Contextual image classification
Segmentation-based object categorization
Computer science
business.industry
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Scale-space segmentation
Pattern recognition
Image segmentation
Support vector machine
Image texture
Bag-of-words model in computer vision
Computer Science::Computer Vision and Pattern Recognition
Segmentation
Computer vision
Artificial intelligence
business
Image resolution
Sub-pixel resolution
Remote sensing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
- Accession number :
- edsair.doi...........6718151a493d7d5e4b2c9727c82eb9f9