A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

Zhang, Ce and Pan, Xin and Li, Huapeng and Gardiner, Andy and Sargent, Isabel and Hare, Jonathon and Atkinson, Peter M. (2018) A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 140. pp. 133-144. ISSN 0924-2716

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The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

Item Type:
Journal Article
Journal or Publication Title:
ISPRS Journal of Photogrammetry and Remote Sensing
Additional Information:
This is the author’s version of a work that was accepted for publication in ISPRS Journal of Photogrammetry and Remote Sensing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in ISPRS Journal of Photogrammetry and Remote Sensing, 140, 2018 DOI: 10.1016/j.isprsjprs.2017.07.014
Uncontrolled Keywords:
?? convolutional neural networkmultilayer perceptronvfsr remotely sensed imageryfusion decisionfeature representationengineering (miscellaneous)atomic and molecular physics, and opticscomputers in earth sciencescomputer science applicationsgeography, plannin ??
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Deposited On:
14 Aug 2017 13:30
Last Modified:
04 Feb 2024 00:44