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Fuzzy Choquet Integration of Deep Convolutional Neural Networks for Remote Sensing

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Computational Intelligence for Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 777))

Abstract

What deep learning lacks at the moment is the heterogeneous and dynamic capabilities of the human system. In part, this is because a single architecture is not currently capable of the level of modeling and representation of the complex human system. Therefore, a heterogeneous set of pathways from sensory stimulus to cognitive function needs to be developed in a richer computational model. Herein, we explore the learning of multiple pathways–as different deep neural network architectures–coupled with appropriate data/information fusion. Specifically, we explore the advantage of data-driven optimization of fusing different deep nets–GoogleNet, CaffeNet and ResNet–at a per class (neuron) or shared weight (single data fusion across classes) fashion. In addition, we explore indices that tell us the importance of each network, how they interact and what aggregation was learned. Experiments are provided in the context of remote sensing on the UC Merced and WHU-RS19 data sets. In particular, we show that fusion is the top performer, each network is needed across the various target classes, and unique aggregations (i.e., not common operators) are learned.

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Notes

  1. 1.

    If \(\mu (X)<1\), properties like idempotency and boundedness are not guaranteed.

  2. 2.

    Due to the maximum (t-conorm) and minimum operators (t-norm), the Sugeno FI does not actually generate any possible number between the minimum and maximum of the inputs. Instead, it selects one of the FM or input values, i.e., at most one of \(2^{N}+N\) values.

  3. 3.

    The ChI is used frequently for various reasons; e.g., it is differentiable [62], for an additive (probability) measure it recovers the Lebesgue integral, it yields a wider spectrum of values between the minimum and maximum (versus the discrete and relatively small number of values that the Sugeno FI selects from), etc.

  4. 4.

    Go to www.derektanderson.com/FuzzyLibrary and www.github.com/scottgs/fi_library.

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Authors and Affiliations

  1. Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA

    Derek T. Anderson, Grant J. Scott & Richard Marcum

  2. Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA

    Muhammad Aminul Islam & Bryce Murray

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  1. Derek T. Anderson
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  1. University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Anderson, D.T., Scott, G.J., Islam, M., Murray, B., Marcum, R. (2018). Fuzzy Choquet Integration of Deep Convolutional Neural Networks for Remote Sensing. In: Pedrycz, W., Chen, SM. (eds) Computational Intelligence for Pattern Recognition. Studies in Computational Intelligence, vol 777. Springer, Cham. https://doi.org/10.1007/978-3-319-89629-8_1

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