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http://dx.doi.org/10.3745/JIPS.02.0180

Building Change Detection Using Deep Learning for Remote Sensing Images  

Wang, Chang (State Key Laboratory of Geo-Information Engineering)
Han, Shijing (School of Natural Resources and Surveying, Nanning Normal University)
Zhang, Wen (School of Civil Engineering, University of Science and Technology Liaoning)
Miao, Shufeng (Wuhan Kedao Geographical Information Engineering Co., Ltd.)
Publication Information
Journal of Information Processing Systems / v.18, no.4, 2022 , pp. 587-598 More about this Journal
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 pre-classifies 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.
Keywords
Deep Neural Network (DNN); Difference Image; Frequency-Domain Significance; Fuzzy C-Means;
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