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Topic Modelling for Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images Based on Multiscale Image Segmentation
Open AccessArticle

Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
Department of Environmental Science, Macquarie University, Sydney, NSW 2109, Australia
Authors to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan and Carlos López-Martínez
Remote Sens. 2017, 9(9), 878;
Received: 22 June 2017 / Revised: 18 August 2017 / Accepted: 21 August 2017 / Published: 23 August 2017
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to count animals. Previous studies have demonstrated the feasibility of using very high resolution multispectral satellite images for animal detection, but to date, the practicality of detecting animals from space using panchromatic imagery has not been proven. This study demonstrates that it is possible to detect and count large mammals (e.g., wildebeests and zebras) from a single, very high resolution GeoEye-1 panchromatic image in open savanna. A novel semi-supervised object-based method that combines a wavelet algorithm and a fuzzy neural network was developed. To discern large mammals from their surroundings and discriminate between animals and non-targets, we used the wavelet technique to highlight potential objects. To make full use of geometric attributes, we carefully trained the classifier, using the adaptive-network-based fuzzy inference system. Our proposed method (with an accuracy index of 0.79) significantly outperformed the traditional threshold-based method (with an accuracy index of 0.58) detecting large mammals in open savanna. View Full-Text
Keywords: GeoEye-1; wavelet transform; fuzzy neural network; remote sensing; conservation GeoEye-1; wavelet transform; fuzzy neural network; remote sensing; conservation
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Xue, Y.; Wang, T.; Skidmore, A.K. Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery. Remote Sens. 2017, 9, 878.

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