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Deep learning for multi-label land cover classification

Zervakis Michalis, Konstantinos Karalas , Grigorios Tsagkatakis , Panagiotis Tsakalides

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/D910C269-0D14-4228-8DAD-66BE60B11916-
Αναγνωριστικόhttps://doi.org/10.1117/12.2195082-
Γλώσσαen-
ΤίτλοςDeep learning for multi-label land cover classificationen
ΔημιουργόςZervakis Michalisen
ΔημιουργόςΖερβακης Μιχαληςel
ΔημιουργόςKonstantinos Karalas en
ΔημιουργόςGrigorios Tsagkatakis en
ΔημιουργόςPanagiotis Tsakalidesen
ΠερίληψηWhereas single class classification has been a highly active topic in optical remote sensing, much less effort has been given to the multi-label classification framework, where pixels are associated with more than one labels, an approach closer to the reality than single-label classification. Given the complexity of this problem, identifying representative features extracted from raw images is of paramount importance. In this work, we investigate feature learning as a feature extraction process in order to identify the underlying explanatory patterns hidden in low-level satellite data for the purpose of multi-label classification. Sparse auto-encoders composed of a single hidden layer, as well as stacked in a greedy layer-wise fashion formulate the core concept of our approach. The results suggest that learning such sparse and abstract representations of the features can aid in both remote sensing and multi-label problems. The results presented in the paper correspond to a novel real dataset of annotated spectral imagery naturally leading to the multi-label formulation. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.en
ΤύποςΑφίσα σε Συνέδριοel
ΤύποςConference Posteren
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-25-
Ημερομηνία Δημοσίευσης2015-
Βιβλιογραφική ΑναφοράK.Karalas , G.Tsagkatakis , M. Zervakis , P. Tsakalides ,"Deep learning for multi-label land cover classification ," in 2015 Image and Signal Proc, for Remote Sen, XXI,doi:10.1117/12.2195082.en

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