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21 March 2018 Residential roof condition assessment system using deep learning
Fan Wang, John P. Kerekes, Zhuoyi Xu, Yandong Wang
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Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016040 (March 2018). https://doi.org/10.1117/1.JRS.12.016040
Abstract
The emergence of high resolution (HR) and ultra high resolution (UHR) airborne remote sensing imagery is enabling humans to move beyond traditional land cover analysis applications to the detailed characterization of surface objects. A residential roof condition assessment method using techniques from deep learning is presented. The proposed method operates on individual roofs and divides the task into two stages: (1) roof segmentation, followed by (2) condition classification of the segmented roof regions. As the first step in this process, a self-tuning method is proposed to segment the images into small homogeneous areas. The segmentation is initialized with simple linear iterative clustering followed by deep learned feature extraction and region merging, with the optimal result selected by an unsupervised index, Q. After the segmentation, a pretrained residual network is fine-tuned on the augmented roof segments using a proposed k-pixel extension technique for classification. The effectiveness of the proposed algorithm was demonstrated on both HR and UHR imagery collected by EagleView over different study sites. The proposed algorithm has yielded promising results and has outperformed traditional machine learning methods using hand-crafted features.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Fan Wang, John P. Kerekes, Zhuoyi Xu, and Yandong Wang "Residential roof condition assessment system using deep learning," Journal of Applied Remote Sensing 12(1), 016040 (21 March 2018). https://doi.org/10.1117/1.JRS.12.016040
Received: 23 August 2017; Accepted: 5 March 2018; Published: 21 March 2018
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Cited by 8 scholarly publications.
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KEYWORDS
Image segmentation
Fluctuations and noise
Image processing algorithms and systems
Image resolution
Feature extraction
Airborne remote sensing
Image classification
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