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A convolutional neural network for automatic analysis of aerial imagery

Maire, F., Mejias, L. and Hodgson, A. (2014) A convolutional neural network for automatic analysis of aerial imagery. In: International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, 24 - 27 November, Wollongong, NSW, Australia pp. 1-8.

Link to Published Version: http://dx.doi.org/10.1109/DICTA.2014.7008084
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Free to read: http://eprints.qut.edu.au/77510/
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Abstract

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Item Type: Conference Paper
Murdoch Affiliation: School of Veterinary and Life Sciences
Publisher: IEEE
Copyright: © 2015 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/25411
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