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Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

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Abstract

This work offers an approach to aerial image classification for use in remote sensing object recognition, image processing and computer vision. Sparse coding (SC) is used to classify unmanned-aerial-vehicle (UAV) and satellite images because SC representation can generalize a large dataset and improve the detection of distinctive features by reducing calculation time for feature matching and classification. Features from images are extracted based on the following descriptors: (a) Scale Invariant Feature Transform; (b) Histogram of Oriented Gradients; and (c) Local Binary Patterns. SC representation and local image features are combined to represent global features for classification. Features are deployed in a sparse model to store descriptor features using extant dictionaries such as (a) the Discrete Cosine Transform and (b) the Discrete Wavelet Transform. An additional two dictionaries are proposed as developed for the present work: (c) the Discrete Ridgelet Transform (DRT) and (d) the Discrete Tchebichef Transform. The DRT dictionary is constructed by using the Ricker wavelet function to generate finite Ridgelet transforms as basis elements for a hybrid dictionary. Different pooling methods have also been employed to convert sparse-coded features into a feature matrix. Various machine learning algorithms are then applied to the feature matrix to classify objects contained in UAV and satellite imagery data. Experimental results show that the SC model secured better accuracy rates for extracted discriminative features contained in remote sensing images. The authors concluded that the proposed SC technique and proposed dictionaries provided feasible solutions for image classification and object recognition.

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Acknowledgements

This project was supported by The Ministery of Energy, Green Technology and Water, Malaysia (Cost Center:AB0315-G07) collaborated with Universiti Teknologi PETRONAS.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia

    Abdul Qayyum, Aamir Saeed Malik, Naufal M. Saad, Mahboob Iqbal, Mohd Faris Abdullah & Waqas Rasheed

  2. UNITEN, Putrajaya Campus, Jalan IKRAM-UNITED, 43000, Kajang, Selangor, Malaysia

    Tuan A. B. Rashid Abdullah

  3. Sabah Electricity Sdn Bhd (SESB), Ground Floor, Wisma Lembaga Letrik Sabah, Jln Tuaran, Karamunsing, 88400, Kota Kinabalu, Sabah, Malaysia

    Mohd Yaqoob Bin Jafaar

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  1. Abdul Qayyum
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  2. Aamir Saeed Malik
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  3. Naufal M. Saad
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Correspondence to Abdul Qayyum.

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Qayyum, A., Saeed Malik, A., Saad, N.M. et al. Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach. Neural Comput & Applic 31, 3587–3607 (2019). https://doi.org/10.1007/s00521-017-3300-5

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