Next Article in Journal
A SAR-Based Index for Landscape Changes in African Savannas
Previous Article in Journal
An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(4), 345; doi:10.3390/rs9040345

A Knowledge-Based Search Strategy for Optimally Structuring the Terrain Dependent Rational Function Models

Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran 19667-15433, Iran
*
Author to whom correspondence should be addressed.
Academic Editors: Guoqing Zhou and Prasad S. Thenkabai
Received: 19 January 2017 / Revised: 19 March 2017 / Accepted: 30 March 2017 / Published: 11 April 2017
View Full-Text   |   Download PDF [1820 KB, uploaded 11 April 2017]   |  

Abstract

Identifying the optimal structure of terrain dependent Rational Function Models (RFMs) not only decreases the number of Ground Control Points (GCPs) required, but also increases the accuracy of the model, by reducing the multi-collinearity of Rational Polynomials Coefficients (RPCs) and avoiding the ill-posed problem. Global optimization algorithms such as Genetic Algorithm (GA), evaluate the different combinations of parameters effectively. Therefore, they have a high ability to detect the optimal structure of RFMs. However, one drawback of these algorithms is their high computation cost. This article proposes a knowledge-based search strategy to overcome this deficiency. The backbone of the proposed method relies on the technical knowledge about the geometric condition of image at the time of acquisition, as well as the effect of external factors such as terrain relief on the image. This method was evaluated on four different datasets, including a SPOT-1A, a SPOT-1B, an IKONOS-Geo image, and a GeoEye-Geo imagery, using various number of GCPs. Experimental results demonstrate the efficiency of the proposed method to achieve a sub-pixel accuracy using few GCPs (only 4–6 points) in all datasets. The results also indicate that the proposed method improves the computation speed by 140 times when comparing with GA. View Full-Text
Keywords: Rational Function Model; terrain dependent; optimal term selection; knowledge-based Rational Function Model; terrain dependent; optimal term selection; knowledge-based
Figures

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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jannati, M.; Valadan Zoej, M.J.; Mokhtarzade, M. A Knowledge-Based Search Strategy for Optimally Structuring the Terrain Dependent Rational Function Models. Remote Sens. 2017, 9, 345.

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