ClusterNet: Unsupervised Generic Feature Learning for Fast Interactive Satellite Image Segmentation

Nicolas Girard (1, 2) , Andrii Zhygallo (1, 2) , Yuliya Tarabalka (3)

1 COMUE UCA - COMUE Université Côte d'Azur (2015-2019)
2 TITANE - Geometric Modeling of 3D Environments
3 LCT - Luxcarta technology [Mouans-Sartoux]

Semantic segmentation on satellite images is used to automatically detect and classify objects of interest over very large areas. Training a neural network for this task generally requires a lot of human-made ground truth classification masks for each object class of interest. We aim to reduce the time spent by humans in the whole process of image segmentation by learning generic features in an unsupervised manner. Those features are then used to leverage sparse human annotations to compute a dense segmentation of the image. This is achieved by essentially labeling groups of semantically similar pixels at once, instead of labeling each pixel almost individually using strokes. While we apply this method to satellite images, our approach is generic and can be applied to any image and to any class of objects on that image.

Mots clés: Clustering; Yuliya Tarabalka: ytarabalka@luxcartacom; Interactive Nicolas Girard: nicolasgirard@inriafr; Satellite Image; Segmentation; Interactive; Deep Learning; Unsupervised

Domaines: Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Apprentissage [cs.LG]; Traitement des images [eess.IV]

Nicolas Girard, Andrii Zhygallo, Yuliya Tarabalka. ClusterNet: Unsupervised Generic Feature Learning for Fast Interactive Satellite Image Segmentation. Image and Signal Processing for Remote Sensing (SPIE), Sep 2019, Strasbourg, France. ⟨hal-02394369v2⟩