Journal article
Classification of Pansharpened Urban Satellite Images
The classification of high resolution urban remote sensing imagery is addressed with the focus on classification of imagery that has been pansharpened by a number of different pansharpening methods. The pansharpening process introduces some spectral and spatial distortions in the resulting fused multispectral image, the amount of which highly varies depending on which pansharpening technique is used.
In the majority of the pansharpening techniques that have been proposed, there is a compromise between the spatial enhancement and the spectral consistency. Here we study the effects of the spectral and spatial distortions on the accuracy in classification of pansharpened imagery. We also study the performance in terms of accuracy of the various pansharpening techniques during classification with spatial information, obtained using mathematical morphology (MM).
MM is used to derive local spatial information from the panchromatic data. Random Forests (RF) and Support Vector Machines (SVM) will be used as classifiers. Experiments are done for three different datasets that have been obtained by two different imaging sensors, IKONOS and QuickBird. These sensors deliver multispectral images that have four bands, R, G, B and near infrared (NIR).
To further study the contribution of the NIR band, experiments are done using both the RGB bands and all four bands, respectively.
Language: | English |
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Publisher: | IEEE |
Year: | 2012 |
Pages: | 281-297 |
ISSN: | 21511535 and 19391404 |
Types: | Journal article |
DOI: | 10.1109/JSTARS.2011.2176467 |
ORCIDs: | Aanaes, Henrik |
Classification Mathematical morphology Morphological profile Pansharpening Spatial consistency Spectral consistency
IKONOS imaging sensor QuickBird imaging sensor Radio frequency Satellites Shape Spatial resolution Support vector machines Vegetation geophysical image processing geophysical techniques mathematical morphology morphological profile panchromatic data pansharpened urban satellite images pansharpening pansharpening methods pansharpening techniques random forests remote sensing spatial consistency spatial distortion spectral consistency spectral distortion support vector machines urban remote sensing imagery