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Remote Sens. 2017, 9(12), 1244; doi:10.3390/rs9121244

Local Deep Hashing Matching of Aerial Images Based on Relative Distance and Absolute Distance Constraints

1
Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Received: 21 September 2017 / Revised: 24 November 2017 / Accepted: 29 November 2017 / Published: 1 December 2017
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Aerial images have features of high resolution, complex background, and usually require large amounts of calculation, however, most algorithms used in matching of aerial images adopt the shallow hand-crafted features expressed as floating-point descriptors (e.g., SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features)), which may suffer from poor matching speed and are not well represented in the literature. Here, we propose a novel Local Deep Hashing Matching (LDHM) method for matching of aerial images with large size and with lower complexity or fast matching speed. The basic idea of the proposed algorithm is to utilize the deep network model in the local area of the aerial images, and study the local features, as well as the hash function of the images. Firstly, according to the course overlap rate of aerial images, the algorithm extracts the local areas for matching to avoid the processing of redundant information. Secondly, a triplet network structure is proposed to mine the deep features of the patches of the local image, and the learned features are imported to the hash layer, thus obtaining the representation of a binary hash code. Thirdly, the constraints of the positive samples to the absolute distance are added on the basis of the triplet loss, a new objective function is constructed to optimize the parameters of the network and enhance the discriminating capabilities of image patch features. Finally, the obtained deep hash code of each image patch is used for the similarity comparison of the image patches in the Hamming space to complete the matching of aerial images. The proposed LDHM algorithm evaluates the UltraCam-D dataset and a set of actual aerial images, simulation result demonstrates that it may significantly outperform the state-of-the-art algorithm in terms of the efficiency and performance. View Full-Text
Keywords: aerial matching; overlap rate; deep learning; local features; hash learning; absolute distance constraints aerial matching; overlap rate; deep learning; local features; hash learning; absolute distance constraints
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, S.; Li, X.; Zhang, Y.; Feng, R.; Zhang, C. Local Deep Hashing Matching of Aerial Images Based on Relative Distance and Absolute Distance Constraints. Remote Sens. 2017, 9, 1244.

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