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Remote Sens. 2017, 9(7), 701; https://doi.org/10.3390/rs9070701

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, Hubei, China
2
Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan
3
China Transport Telecommunications and Information Center, Beijing 100011, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lizhe Wang and Prasad S. Thenkabail
Received: 21 April 2017 / Revised: 13 June 2017 / Accepted: 5 July 2017 / Published: 7 July 2017
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Abstract

When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN) and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM) index. View Full-Text
Keywords: image mosaicking; seamline detection; graph cuts; convolutional neural network; fully convolutional network image mosaicking; seamline detection; graph cuts; convolutional neural network; fully convolutional network
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Li, L.; Yao, J.; Liu, Y.; Yuan, W.; Shi, S.; Yuan, S. Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts. Remote Sens. 2017, 9, 701.

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