@article{scholars12233,
          number = {1},
            note = {cited By 13},
             doi = {10.1080/22797254.2019.1581582},
         journal = {European Journal of Remote Sensing},
           title = {Designing deep CNN models based on sparse coding for aerial imagery: a deep-features reduction approach},
          volume = {52},
       publisher = {Taylor and Francis Ltd.},
           pages = {221--239},
            year = {2019},
            issn = {22797254},
        abstract = {Traditional methods focus on low-level{\^A} handcrafted features representations and it is difficult to design a{\^A} comprehensive classification algorithm for remote sensing scene classification problems. Recently, convolutional neural networks (CNNs) have obtained remarkable performance outcomes, setting several remote sensing benchmarks. Furthermore, direct applications of UAV remote sensing images that use deep convolutional networks are extremely challenging given high input data dimensionality with relatively small amounts of available labelled data. We, therefore, propose a{\^A} CNN approach to scene classification that architecturally incorporates sparse coding (SC) technique for dimension reduction to minimize overfitting. Outcomes were compared with principal component analysis (PCA) and global average pooling (GAP) alternatives that use fully connected layer(s) in pre-trained{\^A} CNN architecture(s) to minimize overfitting. SC was used to encode deep features extracted from the last convolutional layer of pre-trained{\^A} CNN models by using different features maps in which deep features had been converted into low-dimensional{\^A} SC features. These same sparse-coded{\^A} features were concatenated by means of different pooling techniques to obtain global image features for scene classification. The proposed algorithm outperformed current state-of-the-art{\^A} algorithms based on handcrafted features. When using our own UAV-based{\^A} dataset and existing datasets, it was also exceptionally efficient computationally when learning data representations, producing{\^A} a{\^A} 93.64 accuracy rate. {\^A}{\copyright} 2019, {\^A}{\copyright} 2019 The Author(s). Published by Informa UK Limited, trading as Taylor \& Francis Group.},
        keywords = {Aerial photography; Antennas; Benchmarking; Convolution; Convolutional neural networks; Deep learning; Feature extraction; Remote sensing; Unmanned aerial vehicles (UAV), Classification algorithm; Convolutional networks; Dimension reduction; Features reductions; Performance outcome; Scene classification; Sparse coding; UAV remote sensing, Learning algorithms, artificial neural network; design method; image analysis; numerical method; principal component analysis; remote sensing; software; unmanned vehicle},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062424629&doi=10.1080\%2f22797254.2019.1581582&partnerID=40&md5=b6a255f6b0d92c6e5d6d39ab53ba17c3},
          author = {Qayyum, A. and Malik, A. and M Saad, N. and Mazher, M.}
}