We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

A Two-Stream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching

lib:fc4457469f973f25 (v1.0.0)

Vote to reproduce this paper and share portable workflows   1 
Authors: Jae-Hyun Park,Woo-Jeoung Nam,Seong-Whan Lee
ArXiv: 2002.01325
Document:  PDF  DOI 
Artifact development version: GitHub
Abstract URL: https://arxiv.org/abs/2002.01325v1

In this paper, we propose a novel method to precisely match two aerial images that were obtained in different environments via a two-stream deep network. By internally augmenting the target image, the network considers the two-stream with the three input images and reflects the additional augmented pair in the training. As a result, the training process of the deep network is regularized and the network becomes robust for the variance of aerial images. Furthermore, we introduce an ensemble method that is based on the bidirectional network, which is motivated by the isomorphic nature of the geometric transformation. We obtain two global transformation parameters without any additional network or parameters, which alleviate asymmetric matching results and enable significant improvement in performance by fusing two outcomes. For the experiment, we adopt aerial images from Google Earth and the International Society for Photogrammetry and Remote Sensing (ISPRS). To quantitatively assess our result, we apply the probability of correct keypoints (PCK) metric, which measures the degree of matching. The qualitative and quantitative results show the sizable gap of performance compared to the conventional methods for matching the aerial images. All code and our trained model, as well as the dataset are available online.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives


Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!