Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion
Nevrez Imamoglu, Motoki Kimura, Hiroki Miyamoto, Aito Fujita and Ryosuke Nakamura
Abstract
Most of the traditional convolutional neural networks (CNNs) implement bottom-up approach (feed-forward) for image classifications. However, many scientific studies
demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure
for solar power plant detection on middle-resolution satellite images. To express the
strength of the top-down connections, we introduce feedback CNN network (FB-Net) to
a baseline CNN model used for solar power plant classification on multi-spectral satellite
data. Moreover, we introduce a method to improve class activation mapping (CAM) to
our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features
of proposed FB-Net.
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DOI
10.5244/C.31.183
https://dx.doi.org/10.5244/C.31.183
Citation
Nevrez Imamoglu, Motoki Kimura, Hiroki Miyamoto, Aito Fujita and Ryosuke Nakamura. Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion. In T.K. Kim, S. Zafeiriou, G. Brostow and K. Mikolajczyk, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 183.1-183.12. BMVA Press, September 2017.
Bibtex
@inproceedings{BMVC2017_183,
title={Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion},
author={Nevrez Imamoglu, Motoki Kimura, Hiroki Miyamoto, Aito Fujita and Ryosuke Nakamura},
year={2017},
month={September},
pages={183.1-183.12},
articleno={183},
numpages={12},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
doi={10.5244/C.31.183},
isbn={1-901725-60-X},
url={https://dx.doi.org/10.5244/C.31.183}
}