Publication: Scene classification for aerial images based on CNN using sparse coding technique
Date
2017
Authors
Qayyum A.
Malik A.S.
Saad N.M.
Iqbal M.
Faris Abdullah M.
Rasheed W.
Rashid Abdullah T.A.
Bin Jafaar M.Y.
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor and Francis Ltd.
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.
Description
Antennas; Classification (of information); Codes (symbols); Convolution; Image classification; Neural networks; Remote sensing; Satellite imagery; Semantics; Unmanned aerial vehicles (UAV); Convolutional Neural Networks (CNN); Future applications; High resolution remote sensing imagery; Mid-level features; Multi-scale features; Robust performance; Scale invariant feature transforms; Scene classification; Image coding; aircraft; artificial neural network; image classification; satellite imagery; unmanned vehicle