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Open AccessArticle

A Robust Transform Estimator Based on Residual Analysis and Its Application on UAV Aerial Images

by Guorong Cai 1,2, Songzhi Su 3,*, Chengcai Leng 4, Yundong Wu 1,2 and Feng Lu 2,5
1
School of Computer Engineering, Jimei University, Xiamen 360121, China
2
Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China
3
School of Information Science and Technology, Xiamen University, Xiamen 361000, China
4
School of Mathematics, Northwest University, Xi′an 710127, China
5
State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 291; https://doi.org/10.3390/rs10020291
Received: 15 December 2017 / Revised: 1 February 2018 / Accepted: 6 February 2018 / Published: 13 February 2018
(This article belongs to the Special Issue Remote Sensing from Unmanned Aerial Vehicles (UAVs))
Estimating the transformation between two images from the same scene is a fundamental step for image registration, image stitching and 3D reconstruction. State-of-the-art methods are mainly based on sorted residual for generating hypotheses. This scheme has acquired encouraging results in many remote sensing applications. Unfortunately, mainstream residual based methods may fail in estimating the transform between Unmanned Aerial Vehicle (UAV) low altitude remote sensing images, due to the fact that UAV images always have repetitive patterns and severe viewpoint changes, which produce lower inlier rate and higher pseudo outlier rate than other tasks. We performed extensive experiments and found the main reason is that these methods compute feature pair similarity within a fixed window, making them sensitive to the size of residual window. To solve this problem, three schemes that based on the distribution of residuals are proposed, which are called Relational Window (RW), Sliding Window (SW), Reverse Residual Order (RRO), respectively. Specially, RW employs a relaxation residual window size to evaluate the highest similarity within a relaxation model length. SW fixes the number of overlap models while varying the length of window size. RRO takes the permutation of residual values into consideration to measure similarity, not only including the number of overlap structures, but also giving penalty to reverse number within the overlap structures. Experimental results conducted on our own built UAV high resolution remote sensing images show that the proposed three strategies all outperform traditional methods in the presence of severe perspective distortion due to viewpoint change. View Full-Text
Keywords: transform estimation; residual order; 3D reconstruction; camera pose estimation transform estimation; residual order; 3D reconstruction; camera pose estimation
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MDPI and ACS Style

Cai, G.; Su, S.; Leng, C.; Wu, Y.; Lu, F. A Robust Transform Estimator Based on Residual Analysis and Its Application on UAV Aerial Images. Remote Sens. 2018, 10, 291.

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