Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

Rao Muhammad Anwer, Fahad Shahbaz Khan, Joost van de Weijer, Matthieu Molinier, Jorma Laaksonen

    Research output: Contribution to journalArticleScientificpeer-review

    217 Citations (Scopus)


    Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.
    Original languageEnglish
    Pages (from-to)74–85
    JournalISPRS Journal of Photogrammetry and Remote Sensing
    Publication statusPublished - 1 Apr 2018
    MoE publication typeA1 Journal article-refereed


    This work has been funded by the Spanish project TIN2016-79717-R , the CHISTERA project M2CR (PCIN2015-251), SSF through a grant for the project SymbiCloud , VR starting grant (2016-05543), through the Strategic Area for ICT research ELLIIT, CENIIT grant (18.14), the project AIROBEST (317387, 317388) funded by the Academy of Finland , the project MegaMrt2, funded by the Electronic Component Systems for European Leadership (ECSEL) Joint Undertaking (grant agreement No. 737494) of the Horizon 2020 European Union funding programme. We acknowledge the computational resources provided by the Aalto Science-IT project and CSC IT Center for Science, Finland. We also acknowledge the computational support from Nvidia and the NSC.


    • remote sensing
    • deep learning
    • scene classification
    • local binary patterns
    • texture analysis


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