Deep learning decision fusion for the classification of urban remote sensing data

Ghasem Abdi; Farhad Samadzadegan; Peter Reinartz

doi:10.1117/1.JRS.12.016038

13 March 2018 Deep learning decision fusion for the classification of urban remote sensing data

Ghasem Abdi, Farhad Samadzadegan, Peter Reinartz

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016038 (March 2018). https://doi.org/10.1117/1.JRS.12.016038

Abstract

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral–spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

Citation Download Citation

Ghasem Abdi, Farhad Samadzadegan, and Peter Reinartz "Deep learning decision fusion for the classification of urban remote sensing data," Journal of Applied Remote Sensing 12(1), 016038 (13 March 2018). https://doi.org/10.1117/1.JRS.12.016038

Received: 28 November 2017; Accepted: 12 February 2018; Published: 13 March 2018

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