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Article

Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Department of Physical Geography, Faculty of Geoscience, Utrecht University, Princetonlaan 8, 3584 CB Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1343; https://doi.org/10.3390/rs11111343
Received: 3 May 2019 / Revised: 26 May 2019 / Accepted: 31 May 2019 / Published: 4 June 2019
(This article belongs to the Section Remote Sensing Image Processing)
We present a novel convolutional neural network (CNN)-based change detection framework for locating changed building instances as well as changed building pixels from very high resolution (VHR) aerial images. The distinctive advantage of the framework is the self-training ability, which is highly important in deep-learning-based change detection in practice, as high-quality samples of changes are always lacking for training a successful deep learning model. The framework consists two parts: a building extraction network to produce a binary building map and a building change detection network to produce a building change map. The building extraction network is implemented with two widely used structures: a Mask R-CNN for object-based instance segmentation, and a multi-scale full convolutional network for pixel-based semantic segmentation. The building change detection network takes bi-temporal building maps produced from the building extraction network as input and outputs a building change map at the object and pixel levels. By simulating arbitrary building changes and various building parallaxes in the binary building map, the building change detection network is well trained without real-life samples. This greatly lowers the requirements of labeled changed buildings, and guarantees the algorithm’s robustness to registration errors caused by parallaxes. To evaluate the proposed method, we chose a wide range of urban areas from an open-source dataset as training and testing areas, and both pixel-based and object-based model evaluation measures were used. Experiments demonstrated our approach was vastly superior: without using any real change samples, it reached 63% average precision (AP) at the object (building instance) level. In contrast, with adequate training samples, other methods—including the most recent CNN-based and generative adversarial network (GAN)-based ones—have only reached 25% AP in their best cases. View Full-Text
Keywords: building change detection; deep learning; CNN; aerial images building change detection; deep learning; CNN; aerial images
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MDPI and ACS Style

Ji, S.; Shen, Y.; Lu, M.; Zhang, Y. Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples. Remote Sens. 2019, 11, 1343. https://doi.org/10.3390/rs11111343

AMA Style

Ji S, Shen Y, Lu M, Zhang Y. Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples. Remote Sensing. 2019; 11(11):1343. https://doi.org/10.3390/rs11111343

Chicago/Turabian Style

Ji, Shunping, Yanyun Shen, Meng Lu, and Yongjun Zhang. 2019. "Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples" Remote Sensing 11, no. 11: 1343. https://doi.org/10.3390/rs11111343

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