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A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning

European Commission, Joint Research Centre (JRC), Institute for the Protection and Security of the Citizen (IPSC), Global Security and Crisis Management Unit, Via Enrico Fermi 2749, I-21027 Ispra (VA), Italy
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Academic Editors: Jose Moreno and Prasad S. Thenkabail
Remote Sens. 2016, 8(5), 399; https://doi.org/10.3390/rs8050399
Received: 16 December 2015 / Revised: 27 April 2016 / Accepted: 4 May 2016 / Published: 11 May 2016
This work introduces a new classification method in the remote sensing domain, suitably adapted to dealing with the challenges posed by the big data processing and analytics framework. The method is based on symbolic learning techniques, and it is designed to work in complex and information-abundant environments, where relationships among different data layers are assessed in model-free and computationally-effective modalities. The two main stages of the method are the data reduction-sequencing and the association analysis. The former refers to data representation; the latter searches for systematic relationships between data instances derived from images and spatial information encoded in supervisory signals. Subsequently, a new measure named the evidence-based normalized differential index, inspired by the probability-based family of objective interestingness measures, evaluates these associations. Additional information about the computational complexity of the classification algorithm and some critical remarks are briefly introduced. An application of land cover mapping where the input image features are morphological and radiometric descriptors demonstrates the capacity of the method; in this instructive application, a subset of eight classes from the Corine Land Cover is used as the reference source to guide the training phase. View Full-Text
Keywords: automatic classification; remote sensing; symbolic machine learning; supervised learning; association analysis; interestingness measures; big data automatic classification; remote sensing; symbolic machine learning; supervised learning; association analysis; interestingness measures; big data
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MDPI and ACS Style

Pesaresi, M.; Syrris, V.; Julea, A. A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning. Remote Sens. 2016, 8, 399.

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