Ladder Networks for Semi-Supervised Hyperspectral Image Classification

Autor:	Büchel, Julian, Ersoy, Okan
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Statistics - Machine Learning 68T10
Druh dokumentu:	Working Paper
DOI:	10.13140/RG.2.2.33254.27208
Popis:	We used the Ladder Network [Rasmus et al. (2015)] to perform Hyperspectral Image Classification in a semi-supervised setting. The Ladder Network distinguishes itself from other semi-supervised methods by jointly optimizing a supervised and unsupervised cost. In many settings this has proven to be more successful than other semi-supervised techniques, such as pretraining using unlabeled data. We furthermore show that the convolutional Ladder Network outperforms most of the current techniques used in hyperspectral image classification and achieves new state-of-the-art performance on the Pavia University dataset given only 5 labeled data points per class. Comment: Technical Report, 5 pages, 8 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1812.01222 Zobrazit plný text záznamu View this record from Arxiv