Uncertainty Gated Network for Land Cover Segmentation

Autor:	Pascual, Guillem, Seguí, Santi, Vitrià, Jordi
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	The production of thematic maps depicting land cover is one of the most common applications of remote sensing. To this end, several semantic segmentation approaches, based on deep learning, have been proposed in the literature, but land cover segmentation is still considered an open problem due to some specific problems related to remote sensing imaging. In this paper we propose a novel approach to deal with the problem of modelling multiscale contexts surrounding pixels of different land cover categories. The approach leverages the computation of a heteroscedastic measure of uncertainty when classifying individual pixels in an image. This classification uncertainty measure is used to define a set of memory gates between layers that allow a principled method to select the optimal decision for each pixel. Comment: Accepted in CVPR18 workshop: "DeepGlobe: A Challenge for Parsing the Earth through Satellite Images"
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1805.11348 Zobrazit plný text záznamu View this record from Arxiv