Urban objects recognition feasibilities by airborne hyperspectral and multispectral remote sensing

Abstract : This paper explores the recognition uncertainty of urban objects by multiband imagery. The purpose is to recognize the urban objects by their spectral signature, using an external spectral library. Two Vis-NIR images were used for the study: a four bands Kompsat-2 multispectral image and a 16 bands Ricola‘s airborne hyperspectral image, two supervised classifiers were tested; a spectral based classifier, called the Spectral Angle Mapper (SAM), coupled to an external spectral library and a machine learning based classifier called the Support Vector Machine (SVM), in a second step the classification results obtained by the two classifiers were merged, the goal was to take advantage of both techniques, to optimize the classification result. The classifiers performance and the objects recognition feasibility were discussed for both images.
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https://hal-amu.archives-ouvertes.fr/hal-01352525
Contributor : Sébastien Gadal <>
Submitted on : Monday, August 8, 2016 - 12:41:47 PM
Last modification on : Tuesday, September 10, 2019 - 8:32:14 PM

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  • HAL Id : hal-01352525, version 1

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Sébastien Gadal, Walid Ouerghemmi. Urban objects recognition feasibilities by airborne hyperspectral and multispectral remote sensing. Spatial Accuracy 2016, Jul 2016, Montpellier, France. pp.101-108. ⟨hal-01352525⟩

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