| Autor: |
Mezouar, Oussama, Meskine, Fatiha, Boukerch, Issam, Taleb, Nasreddine |
| Předmět: |
|
| Zdroj: |
International Journal of Remote Sensing; Nov 2021, Vol. 42 Issue 21, p8056-8076, 21p |
| Abstrakt: |
The rational function model (RFM) is widely used for high-resolution satellite imagery allowing its use in GIS and remote sensing applications. RFM can be seen as a non-parametric model that establishes a geometric transformation between image and ground coordinate spaces. Unlike the rigorous models, it constitutes an easy and simple approach where any prior knowledge about the sensor's physical configuration and parameters is required. For a precise ortho-rectification, RFM requires a large number of accurate and well-distributed ground control points (GCPs) which is a time-consuming and expensive process. Moreover, adjustment errors may occur due to the over-parameterization resulting from the high number of coefficients in this model. In fact, coefficients in RFM do not have any physical meaning, which makes it impossible to find their best combination. In order to overcome this problem, optimization based on evolutionary algorithms seems to be an appropriate solution. In this paper, a hybrid binary of particle swarm optimization (PSO) method is proposed. It combines the PSO concept with that of genetic algorithms by adding two new operations in the binary version of PSO consisting of crossover and mutation in order to increase the convergence speed and avoid the local optimum phenomenon. The proposed algorithm has been applied on two data sets provided by the Alsat-2 Algerian satellite in different configurations, namely standard scenes and strips which are a group of scenes acquired continuously from the same orbit. To the best of our knowledge, this is the first work dealing with the use of PSO for the RFM optimization aiming at the geometrical modelling of a strip of images. The obtained results have shown that the proposed HPSO-RFM method outperforms other competing methods by achieving, for the best scenario, an accuracy improvement of about 38% and 21% over PSORFO and PSO-KFCV, respectively. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
