Building Change Detection Using Deep Learning for Remote Sensing Images


Chang Wang, Shijing Han, Wen Zhang, and Shufeng Miao, Journal of Information Processing Systems Vol. 18, No. 4, pp. 587-598, Aug. 2022  

10.3745/JIPS.02.0180
Keywords: Deep neural network (DNN), Difference Image, Frequency-Domain Significance, fuzzy c-means
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Abstract

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique preclassifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.


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Cite this article
[APA Style]
Chang Wang, Shijing Han, Wen Zhang, & and Shufeng Miao (2022). Building Change Detection Using Deep Learning for Remote Sensing Images. Journal of Information Processing Systems, 18(4), 587-598. DOI: 10.3745/JIPS.02.0180.

[IEEE Style]
C. Wang, S. Han, W. Zhang and a. S. Miao, "Building Change Detection Using Deep Learning for Remote Sensing Images," Journal of Information Processing Systems, vol. 18, no. 4, pp. 587-598, 2022. DOI: 10.3745/JIPS.02.0180.

[ACM Style]
Chang Wang, Shijing Han, Wen Zhang, and and Shufeng Miao. 2022. Building Change Detection Using Deep Learning for Remote Sensing Images. Journal of Information Processing Systems, 18, 4, (2022), 587-598. DOI: 10.3745/JIPS.02.0180.