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Open AccessArticle

Fusion Network for Change Detection of High-Resolution Panchromatic Imagery

Department of Computer Engineering, Kwangwoon University, Seoul 139701, Korea
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Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(7), 1441; https://doi.org/10.3390/app9071441
Received: 28 January 2019 / Revised: 24 March 2019 / Accepted: 2 April 2019 / Published: 5 April 2019
This paper proposes a fusion network for detecting changes between two high-resolution panchromatic images. The proposed fusion network consists of front- and back-end neural network architectures to generate dual outputs for change detection. Two networks for change detection were applied to handle image- and high-level changes of information, respectively. The fusion network employs single-path and dual-path networks to accomplish low-level and high-level differential detection, respectively. Based on two dual outputs, a two-stage decision algorithm was proposed to efficiently yield the final change detection results. The dual outputs were incorporated into the two-stage decision by operating logical operations. The proposed algorithm was designed to incorporate not only dual network outputs but also neighboring information. In this paper, a new fused loss function was presented to estimate the errors and optimize the proposed network during the learning stage. Based on our experimental evaluation, the proposed method yields a better detection performance than conventional neural network algorithms, with an average area under the curve of 0.9709, percentage correct classification of 99%, and Kappa of 75 for many test datasets. View Full-Text
Keywords: change detection; convolutional network; deep learning; panchromatic; remote sensing change detection; convolutional network; deep learning; panchromatic; remote sensing
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Wiratama, W.; Sim, D. Fusion Network for Change Detection of High-Resolution Panchromatic Imagery. Appl. Sci. 2019, 9, 1441.

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