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Fusion of LBP and Hu-Moments with Fisher Vectors in Remote Sensing Imagery

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Computational Collective Intelligence (ICCCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11683))

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Abstract

There are huge volumes of scene images generated periodically by satellite technology which require effective processing for intelligent decisions. The satellite sensed images contain diverse image contents such as shape, color, texture, spectral, and spatial resolutions. These variables are further affected with varying illumination conditions which result to noisy images. This poses a challenge to image analysis methods thus limiting their capabilities tasks of image understanding and interpretation. Remote sensing imagery classification task hence need advanced methods which can characterize images better so as to achieve higher accuracy results on scene images classification. This research proposes a feature-fusion-strategy of complementary image features, this is, Local Binary Patterns (LBP) and Hu Moments are fused with fisher vectors and the resultant is more discriminative feature representation that yield better image scene classification accuracy of 50.12% as demonstrated in with experimental results. This is an improvement compared to some pixel-based image descriptor methods in literature that are implemented. Although there are other methods in literature with superior classification accuracies than the proposed method, its is evident that fusion of complementary-features result to better classification accuracy.

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Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Westville Campus, Durban, 4000, South Africa

    Ronald Tombe & Serestina Viriri

Authors
  1. Ronald Tombe
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  2. Serestina Viriri
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Corresponding author

Correspondence to Serestina Viriri .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l’Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l’Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l’Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Tombe, R., Viriri, S. (2019). Fusion of LBP and Hu-Moments with Fisher Vectors in Remote Sensing Imagery. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_33

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  • DOI: https://doi.org/10.1007/978-3-030-28377-3_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28376-6

  • Online ISBN: 978-3-030-28377-3

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