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

Zhang, Ce, Pan, Xin, Li, Huapeng, Gardiner, Andy, Sargent, Isabel, Hare, Jonathon and Atkinson, Peter M. (2017) A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification ISPRS Journal of Photogrammetry and Remote Sensing (doi:10.1016/j.isprsjprs.2017.07.014).

Record type: Article

Abstract

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.

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More information

Accepted/In Press date: 30 July 2017
e-pub ahead of print date: 2 August 2017

Identifiers

Local EPrints ID: 413316
URI: http://eprints.soton.ac.uk/id/eprint/413316
ISSN: 0924-2716
PURE UUID: fb57d860-b414-4610-b17e-18fb5cb4d6bc
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283

Catalogue record

Date deposited: 21 Aug 2017 16:31
Last modified: 21 Aug 2017 16:31

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Contributors

Author: Ce Zhang
Author: Xin Pan
Author: Huapeng Li
Author: Andy Gardiner
Author: Isabel Sargent
Author: Jonathon Hare ORCID iD
Author: Peter M. Atkinson

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