Watershed Segmentation of Remotely Sensed Images Based on a Supervised Fuzzy Pixel Classification

Sébastien Derivaux (1) , Sébastien Lefèvre (1) , Cédric Wemmert (1) , Jerzy Korczak (1)

1 LSIIT - Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection

Remotely sensed images are more and more precise (spatial resolution under 1 meter). For these images, objects of interest contains several pixels. Generally a segmentation method is used to cluster pixels that belong to the same objects before classification. The quality of such a segmentation method is crucial to achieve good clasification results. In this paper, a new segmentation method is proposed which aims to improve the classical watershed segmentation method based on multispectral gradient. The proposed method uses some labeled samples with classes of interest to induce a new dissimilarity between pixels which defines a new representation space to be used.

Domaines: Traitement des images [eess.IV]

Sébastien Derivaux, Sébastien Lefèvre, Cédric Wemmert, Jerzy Korczak. Watershed Segmentation of Remotely Sensed Images Based on a Supervised Fuzzy Pixel Classification. IEEE International Geosciences And Remote Sensing Symposium (IGARSS), 2006, United States. pp.3712-3715, ⟨10.1109/IGARSS.2006.951⟩. ⟨hal-00516090⟩