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Feature extraction and classification of VHR images with attribute profiles and convolutional neural networks

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Abstract

Effective feature extraction plays an important role in the classification of very high resolution (VHR) remote sensing (RS) images. Current researches mainly focus on individual shallow or deep feature extraction methods, remarkable representatives of which include Morphological Attribute Profiles (APs) and Convolutional Neural Networks (CNNs). Actually, to combine low-level and high-level features may take advantages of each approach and fully exploit the description capability. In this paper, APs and CNNs are integrated to characterize VHR RS images in order to improve the pixel description. Moreover, during the training of CNNs, regularization, dropout and fine-tuning strategies are all utilized to mitigate over-fitting problems due to insufficient samples in RS applications. Evaluations using QuickBird datasets demonstrate that our proposed method leads to a higher classification accuracy compared to individual method for VHR images.

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Acknowledgements

We would like to thank Prof. Yun Zhang, Prof. Hengjian Tong and CFB Gagetown for providing the QuickBird images, and thank Prof. Mauro Dalla Mura for kindly sharing the implementation of attribute profiles. We also want to thank all the anonymous reviewers who provided constructive comments to help us improve the quality of this manuscript. And this work is supported by the National Natural Science Foundation under Grant 41701417, China Postdoctoral Science Foundation under Grant 2016M602390, the Provincial Natural Science Foundation of Hubei under Grant 2016CFB278, the Open Research Project of Hubei Key Laboratory of Intelligent Geo-Information Processing (KLIGIP1608), and the fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan).

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Authors and Affiliations

  1. Hubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, No.388 Lumo Road, Wuhan, 430074, China

    Tian Tian, Lang Gao & Weijing Song

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, 78249, USA

    Kim-Kwang Raymond Choo

  3. Base of the State Laboratory of Urban Environmental Processes and Digital Modeling, Key Lab of Resources Environment and GIS of Beijing, Capital Normal University, Beijing, 100048, China

    Jijun He

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  1. Tian Tian
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Correspondence to Jijun He.

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Tian, T., Gao, L., Song, W. et al. Feature extraction and classification of VHR images with attribute profiles and convolutional neural networks. Multimed Tools Appl 77, 18637–18656 (2018). https://doi.org/10.1007/s11042-017-5331-4

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