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arXiv · 1701.06462

Using Convolutional Neural Networks to Count Palm Trees in Satellite Images

Eu Koon Cheang·Teik Koon Cheang·Yong Haur Tay
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Abstract

In this paper we propose a supervised learning system for counting and localizing palm trees in high-resolution, panchromatic satellite imagery (40cm/pixel to 1.5m/pixel). A convolutional neural network classifier trained on a set of palm and no-palm images is applied across a satellite image scene in a sliding window fashion. The resultant confidence map is smoothed with a uniform filter. A non-maximal suppression is applied onto the smoothed confidence map to obtain peaks. Trained with a small dataset of 500 images of size 40x40 cropped from satellite images, the system manages to achieve a tree count accuracy of over 99%.

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Eu Koon Cheang, Teik Koon Cheang, Yong Haur Tay. 2017-01-23. Using Convolutional Neural Networks to Count Palm Trees in Satellite Images. https://arxiv.org/abs/1701.06462

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