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Open AccessArticle

End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

1
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1382; https://doi.org/10.3390/rs11111382
Received: 10 May 2019 / Revised: 6 June 2019 / Accepted: 8 June 2019 / Published: 10 June 2019
(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)
Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods. View Full-Text
Keywords: change detection; deep learning; end-to-end; encoder-decoder architecture; feature maps; multiple side-outputs fusion change detection; deep learning; end-to-end; encoder-decoder architecture; feature maps; multiple side-outputs fusion
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MDPI and ACS Style

Peng, D.; Zhang, Y.; Guan, H. End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sens. 2019, 11, 1382. https://doi.org/10.3390/rs11111382

AMA Style

Peng D, Zhang Y, Guan H. End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sensing. 2019; 11(11):1382. https://doi.org/10.3390/rs11111382

Chicago/Turabian Style

Peng, Daifeng; Zhang, Yongjun; Guan, Haiyan. 2019. "End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++" Remote Sens. 11, no. 11: 1382. https://doi.org/10.3390/rs11111382

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