A robust matching network for gradually estimating geometric transformation on remote sensing imagery

Dong Geon Kim, Woo Jeoung Nam, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a matching network for gradually estimating the geometric transformation parameters between two aerial images taken in the same area but in different environments. To precisely matching two aerial images, there are important factors to consider such as different time, a variation of viewpoint, size, and rotation. The conventional methods for matching aerial image pairs with the large variations are extremely time-consuming process and have the limitations finding correct correspondences, because the image gradient and grayscale intensity for generating the feature descriptors are not robust to the variations. We design the network architecture as an end-to-end trainable deep neural network to reflect the characteristics of aerial images. The hierarchical structures that orderly estimate the rotation and the affine transformations make it possible to reduce the range of predictions and minimize errors caused by misalignment, resulting in more precise matching performance. Furthermore, we apply transfer learning to make the feature extraction networks more robust and suitable for the aerial image domain with the large variations. For the experiment, we apply the remote sensing image datasets from Google Earth and International Society for Photogrammetry and Remote Sensing (ISPRS). To evaluate our method quantitatively, we measure the probability of correct keypoints (PCK) metrics for objectively comparing the degree of matching. In terms of qualitative and quantitative assessment, our method demonstrates the state-of-the-art performances compared to the existing methods.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3889-3894
Number of pages6
ISBN (Electronic)9781728145693
DOIs
Publication statusPublished - 2019 Oct
Event2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy
Duration: 2019 Oct 62019 Oct 9

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2019-October
ISSN (Print)1062-922X

Conference

Conference2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
CountryItaly
CityBari
Period19/10/619/10/9

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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