This paper deals with a comparison of different colour space in order to improve high resolution images classification.
The background of this study is the measure of the agriculture impact on the environment in islander context.
Biodiversity is particularly sensitive and relevant in such areas and the follow-up of the forest front is a way to ensure its
preservation. Very high resolution satellite images are used such as QuickBird and IKONOS scenes.
In order to segment the images into forest and agriculture areas, we characterize both ground covers with colour and
texture features. A classical unsupervised classifier is then used to obtain labelled areas. As features are computed on
coloured images, we can wonder if the colour space choice is relevant. This study has been made considering more than
fourteen colour spaces (RGB, YUV, Lab, YIQ, YCrCs, XYZ, CMY, LMS, HSL, KLT, IHS, I1I2I3, HSV, HSI, etc.) and shows
the visual and quantitative superiority of IHS on all others. For conciseness reasons, results only show RGB, I1I2I3 and
IHS colour spaces.
Conference Committee Involvement (2)
Image and Signal Processing for Remote Sensing
31 August 2009 | Berlin, Germany
Image and Signal Processing for Remote Sensing
15 September 2008 | Cardiff, Wales, United Kingdom
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