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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/D910C269-0D14-4228-8DAD-66BE60B11916 | - |
| Identifier | https://doi.org/10.1117/12.2195082 | - |
| Language | en | - |
| Title | Deep learning for multi-label land cover classification | en |
| Creator | Zervakis Michalis | en |
| Creator | Ζερβακης Μιχαλης | el |
| Creator | Konstantinos Karalas | en |
| Creator | Grigorios Tsagkatakis | en |
| Creator | Panagiotis Tsakalides | en |
| Content Summary | Whereas 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 Item | Αφίσα σε Συνέδριο | el |
| Type of Item | Conference Poster | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-10-25 | - |
| Date of Publication | 2015 | - |
| Bibliographic Citation | K.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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