![Institutional Repository [SANDBOX]](images/dias.logo.png "Institutional Repository [SANDBOX]"){kind=logo}
Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/D910C269-0D14-4228-8DAD-66BE60B11916 | ||
| Year | 2015 | ||
| Type of Item | Conference Poster | ||
| License |
|
||
| 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. https://doi.org/10.1117/12.2195082 | ||
| Appears in Collections |
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.
Copyright © DIAS 2013
