Next Article in Journal
A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles
Previous Article in Journal
Biodiversity Monitoring in Changing Tropical Forests: A Review of Approaches and New Opportunities
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(10), 1053; https://doi.org/10.3390/rs9101053

An Emergency Georeferencing Framework for GF-4 Imagery Based on GCP Prediction and Dynamic RPC Refinement

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 5 September 2017 / Revised: 11 October 2017 / Accepted: 12 October 2017 / Published: 17 October 2017
(This article belongs to the Section Remote Sensing Image Processing)
View Full-Text   |   Download PDF [6750 KB, uploaded 24 October 2017]   |  

Abstract

GaoFen-4 (GF-4) imagery has very potential in terms of emergency response due to its gazing mode. However, only poor geometric accuracy can be obtained using the rational polynomial coefficient (RPC) parameters provided, making ground control points (GCPs) necessary for emergency response. However, selecting GCPs is traditionally time-consuming, labor-intensive, and not fully reliable. This is mainly due to the facts that (1) manual GCP selection is time-consuming and cumbersome because of too many human interventions, especially for the first few GCPs; (2) typically, GF-4 gives planar array imagery acquired at rather large tilt angles, and the distortion introduces problems in image matching; (3) reference data will not always be available, especially under emergency circumstances. This paper provides a novel emergency georeferencing framework for GF-4 Level 1 imagery. The key feature is GCP prediction based on dynamic RPC refinement, which is able to predict even the first GCP and the prediction will be dynamically refined as the selection goes on. This is done by two techniques: (1) GCP prediction using RPC parameters and (2) dynamic RPC refinement using as few as only one GCP. Besides, online map services are also adopted to automatically provide reference data. Experimental results show that (1) GCP predictions improve using dynamic RPC refinement; (2) GCP selection becomes more efficient with GCP prediction; (3) the integration of online map services constitutes a good example for emergency response. View Full-Text
Keywords: GF-4; georeferencing; emergency response; online map services; ground control point (GCP) prediction; dynamic rational polynomial coefficient (RPC) refinement GF-4; georeferencing; emergency response; online map services; ground control point (GCP) prediction; dynamic rational polynomial coefficient (RPC) refinement
Figures

Graphical abstract

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Li, P.; Sun, K.; Li, D.; Sui, H.; Zhang, Y. An Emergency Georeferencing Framework for GF-4 Imagery Based on GCP Prediction and Dynamic RPC Refinement. Remote Sens. 2017, 9, 1053.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top