Please use this identifier to cite or link to this item:
Title: Scene classification for aerial images based on CNN using sparse coding technique
Authors: Qayyum, A. 
Malik, A.S. 
Saad, N.M. 
Iqbal, M. 
Faris Abdullah, M. 
Rasheed, W. 
Rashid Abdullah, T.A. 
Bin Jafaar, M.Y. 
Issue Date: 2017
Abstract: Aerial scene classification purposes to automatically label aerial images with specific semantic categories. However, cataloguing presents a fundamental problem for high-resolution remote-sensing imagery (HRRS). Recent developments include several approaches and numerous algorithms address the task. This article proposes a convolutional neural network (CNN) approach that utilizes sparse coding for scene classification applicable for HRRS unmanned aerial vehicle (UAV) and satellite imagery. The article has two major sections: the first describes the extraction of dense multiscale features (multiple scales) from the last convolutional layer of a pre-trained CNN models; the second describes the encoding of extracted features into global image features via sparse coding to achieve scene classification. The authors compared experimental outcomes with existing techniques such as Scale-Invariant Feature Transform and demonstrated that features from pre-trained CNNs generalized well with HRRS datasets and were more expressive than low- and mid-level features, exhibiting an overall 90.3% accuracy rate for scene classification compared to 85.4% achieved by SIFT with sparse coding. Thus, the proposed CNN-based sparse coding approach obtained a robust performance that holds promising potential for future applications in satellite and UAV imaging. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
DOI: 10.1080/01431161.2017.1296206
Appears in Collections:COE Scholarly Publication

Show full item record

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.