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Deep learning for multi-label land cover classification

Zervakis Michalis, Konstantinos Karalas , Grigorios Tsagkatakis , Panagiotis Tsakalides

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URIhttp://purl.tuc.gr/dl/dias/D910C269-0D14-4228-8DAD-66BE60B11916-
Identifierhttps://doi.org/10.1117/12.2195082-
Languageen-
TitleDeep learning for multi-label land cover classificationen
CreatorZervakis Michalisen
CreatorΖερβακης Μιχαληςel
CreatorKonstantinos Karalas en
CreatorGrigorios Tsagkatakis en
CreatorPanagiotis Tsakalidesen
Content SummaryWhereas 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
Type of ItemΑφίσα σε Συνέδριοel
Type of ItemConference Posteren
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-25-
Date of Publication2015-
Bibliographic CitationK.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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