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Open AccessArticle

Local Deep Descriptor for Remote Sensing Image Feature Matching

1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Institute for Cartography, TU Dresden, 01062 Dresden, Germany
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(4), 430; https://doi.org/10.3390/rs11040430
Received: 3 January 2019 / Revised: 2 February 2019 / Accepted: 15 February 2019 / Published: 19 February 2019
Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network. View Full-Text
Keywords: feature matching; deep descriptor network; image registration; feature descriptor; deep learning feature matching; deep descriptor network; image registration; feature descriptor; deep learning
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Dong, Y.; Jiao, W.; Long, T.; Liu, L.; He, G.; Gong, C.; Guo, Y. Local Deep Descriptor for Remote Sensing Image Feature Matching. Remote Sens. 2019, 11, 430.

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