Rational function modeling for spaceborne SAR datasets

Zhang, Lu; He, Xueyan; Balz, Timo; Wei, Xiaofeng; Liao, Mingsheng

doi:10.1016/j.isprsjprs.2010.10.007

Cited by 41 publications

(24 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous research studies have been carried out on investigation of the empirical models in the last decade He 2015, 2014;Gil et al 2014;Teo 2011;Valadan Zoej et al 2007;Fraser and Hanley 2003;Dowman and Tao 2002;Toutin 2006;Valadan Zoej 1997;Tao and Hu 2001;Valadan Zoej et al 2002). In this regard, rational function models (RFMs) are highlighted as accurate and most applicable empirical methods for georeferencing of satellite imagery (Zhang et al 2011;Shaker, Yan, and Easa 2010). The results of a RFM are often as accurate as physical models and there is no need for interior or exterior orientation parameters for solving the model (Aguilar, Saldana, and Aguilar 2013;Zhang et al 2011;Valadan Zoej et al 2007;Fraser and Hanley 2003;Toutin 2006;Tao and Hu 2001;Paderes, Mikhail, and Fagerman 1989;Greve, Molander, and Gordon 1992;OGC 1999;Toutin and Cheng 2000).…”

Section: Please Scroll Down For Articlementioning

confidence: 99%

“…In this regard, rational function models (RFMs) are highlighted as accurate and most applicable empirical methods for georeferencing of satellite imagery (Zhang et al 2011;Shaker, Yan, and Easa 2010). The results of a RFM are often as accurate as physical models and there is no need for interior or exterior orientation parameters for solving the model (Aguilar, Saldana, and Aguilar 2013;Zhang et al 2011;Valadan Zoej et al 2007;Fraser and Hanley 2003;Toutin 2006;Tao and Hu 2001;Paderes, Mikhail, and Fagerman 1989;Greve, Molander, and Gordon 1992;OGC 1999;Toutin and Cheng 2000). RFMs are of interest not only for image providers in order to keep the physical parameters of the sensors unrevealed but also for image users because of their straightforward computations.…”

Section: Please Scroll Down For Articlementioning

confidence: 99%

See 1 more Smart Citation

Introducing genetic modification concept to optimize rational function models (RFMs) for georeferencing of satellite imagery

Jannati

Zoej

2015

GIScience & Remote Sensing

View full text Add to dashboard Cite

Genetic algorithms (GAs) are frequently used for optimization of remote sensing models. Recently, they have been used in optimization of rational function models (RFMs) for georeferencing of satellite images. In this way, fewer ground control points (GCPs) are needed while accurate results are achieved in comparison to manual or try and error based approaches of terrain dependent RFM term selection. However, GAs are quite inefficient in terms of computational speed. In this article, a novel optimization approach adopting a newly introduced concept in natural sciences called 'genetic modification' is proposed to speed up the basic GA. According to the proposed method, a qualification coefficient is defined to examine the qualification of individual genes. Therefore, qualified genes are identified and are used to produce a new set of chromosomes in each iteration of the algorithm as 'transgenic chromosomes'. Considering these chromosomes as a part of parents for next generation, desired characteristics (optimal parameters) appeared with an efficient speed. To evaluate the performance of the proposed algorithm, over two different case studies, RFM is optimized using both proposed and basic GAs. The results indicate that the optimization speed is improved by 20 times, while the accuracies are preserved.

show abstract

Section: Please Scroll Down For Articlementioning

confidence: 99%

Section: Please Scroll Down For Articlementioning

confidence: 99%

Introducing genetic modification concept to optimize rational function models (RFMs) for georeferencing of satellite imagery

Jannati

Zoej

2015

GIScience & Remote Sensing

View full text Add to dashboard Cite

show abstract

“…From the SLC to GR level of processing, the acquired data are already subjected to terrain correction, range cell migration, etc., along with the spacecraft orbit and attitude ephemeris data to realize system-level geometrically corrected images. As pointed out by Zhang, He, et al (2011), geometric accuracy in the final output is strongly dependent on levels of processing the datasets.…”

Section: Introductionmentioning

confidence: 99%

“…edges, corners of stereo pair images) of additional control points (CNPs). In another work, Zhang, He, et al (2011) applied the RPC model to spaceborne SAR datasets with different processing levels acquired by various SAR satellites. They observed that the products of different processing levels produce dissimilar geometric accuracies.…”

Section: Introductionmentioning

confidence: 99%

Geocoding RISAT-1 MRS images using bias-compensated RPC models

Sekhar

Kumar

Dadhwal

2014

International Journal of Remote Sensing

View full text Add to dashboard Cite

The purpose of this paper is two-fold. First, the use of the rational polynomial coefficients (RPCs) model is studied for geocoding of Medium Resolution Scan (MRS) ground range (GR) images from the RISAT-1 SAR mission. As the GR images are obtained after many preprocessing image corrections for topographic effect, range cell migration, etc., the number of ground control points (GCPs) required for orthorectification to meet desired geometric quality needs to be established. This assumes importance due to difficulty in visual identification of the GCPs in 18 m-resolution MRS SAR images. Second, three possible methods of bias-compensated RPC models are studied for geocoding. These cases are (A) modified RPC with shift bias, (B) regenerated RPC with shift bias, and (C) regenerated RPC with affine transform model. Experiments are carried out with a set of eight scenes acquired over planar regions especially to avoid the impact of SAR-specific geometric effects such as foreshortening and layover. Geometric accuracy of the orthoimages obtained from these cases is verified at GCPs used for processing as control points and at new GCPs used as check points. It is observed that the modified RPC with the shift bias case required more GCPs to meet the desired geopositioning accuracy. Even though both the regenerated RPC models have shown near similar performance, the regenerated RPC with shift bias compensation is found to reach the required geopositioning accuracy with least number of the GCPs, suggesting it as a strong candidate for realizing operational high precision RISAT-1 geocoded products for multi-temporal data analysis.

show abstract

“…Physical or rigorous sensor models, by which high geometric accuracy can be obtained, fully consider the procedure of the geometric imaging (Tao & Hu, 2001;Zhang et al, 2011); therefore, each of the model's parameter has a physical meaning. Although physical models have the aforementioned benefits, they have their own drawbacks, including model complexity and sensor dependency, that is, each sensor has its own unique model (Long et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Statistical Method to Overcome Overfitting Issue in Rational Function Models

Moghaddam

Mokhtarzade

Naeini

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Rational function models (RFMs) are known as one of the most appealing models which are extensively applied in geometric correction of satellite images and map production. Overfitting is a common issue, in the case of terrain dependent RFMs, that degrades the accuracy of RFMs-derived geospatial products. This issue, resulting from the high number of RFMs' parameters, leads to ill-posedness of the RFMs. To tackle this problem, in this study, a fast and robust statistical approach is proposed and compared to Tikhonov regularization (TR) method, as a frequently-used solution to RFMs' overfitting. In the proposed method, a statistical test, namely, significance test is applied to search for the RFMs' parameters that are resistant against overfitting issue. The performance of the proposed method was evaluated for two real data sets of Cartosat-1 satellite images. The obtained results demonstrate the efficiency of the proposed method in term of the achievable level of accuracy. This technique, indeed, shows an improvement of 50-80% over the TR.

show abstract

Rational function modeling for spaceborne SAR datasets

Cited by 41 publications

References 27 publications

Introducing genetic modification concept to optimize rational function models (RFMs) for georeferencing of satellite imagery

Introducing genetic modification concept to optimize rational function models (RFMs) for georeferencing of satellite imagery

Geocoding RISAT-1 MRS images using bias-compensated RPC models

Statistical Method to Overcome Overfitting Issue in Rational Function Models

Contact Info

Product

Resources

About