A competitive pixel-object approach for land cover classification
| Autor: | Mingjun Song, Daniel L. Civco, James D. Hurd |
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| Rok vydání: | 2005 |
| Předmět: |
Similarity (geometry)
Artificial neural network Pixel Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image segmentation Land cover Object (computer science) ComputingMethodologies_PATTERNRECOGNITION Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition General Earth and Planetary Sciences Segmentation Computer vision Artificial intelligence business |
| Zdroj: | International Journal of Remote Sensing. 26:4981-4997 |
| ISSN: | 1366-5901 0143-1161 |
| DOI: | 10.1080/01431160500213912 |
| Popis: | This paper describes a novel remote sensing land cover classification approach named competitive pixel-object classification, based on Bayesian neural networks and image segmentation. This approach makes use of both pixel spectral features and object features resulting from image segmentation through a competitive mechanism to resolve the problem of spectral confusion caused by reflectance similarity of some land cover types that traditional pixel-based classification cannot resolve. The competitive pixel-object method reduces the unreliability of object feature information produced by over- or under-segmentation of the image through a competitive mechanism. The experiment shows that the competitive pixel-object approach produces higher classification accuracy than either pixel-based classification or object-oriented classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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