Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery

In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near infrared images. The novelty of our scheme is that we employ only an extremely small portion of labeled data for training...

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Autors principals:	Eftychios Protopapadakis, Anastasios Doulamis, Nikolaos Doulamis, Evangelos Maltezos
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2021-01-01
Col·lecció:	Remote Sensing
Matèries:	semi-supervised learning deep learning stack autoencoders building detection remote sensing semantic segmentation
Accés en línia:	https://www.mdpi.com/2072-4292/13/3/371
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