Colour space influence for vegetation image classification Application to Caribbean forest and agriculture

Mohamed Abadi (1) , Enguerran Grandchamp (2)

1 SIC
2 LAMIA - Laboratoire de Mathématiques Informatique et Applications

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.

Mots clés: colour space; Classification; texture features; Gabor filters; Hu moments; high resolution images

Domaines: Traitement des images [eess.IV]; Ingénierie de l'environnement

Mohamed Abadi, Enguerran Grandchamp. Colour space influence for vegetation image classification Application to Caribbean forest and agriculture. SPIE Remote Sensing, Sep 2008, Cardiff, United Kingdom. ⟨10.1117/12.799886⟩. ⟨hal-00602277⟩