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15 October 2015 Deep learning for multi-label land cover classification
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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.
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Konstantinos Karalas, Grigorios Tsagkatakis, Michalis Zervakis, and Panagiotis Tsakalides "Deep learning for multi-label land cover classification", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430Q (15 October 2015);

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