Geometry Based Co-Registration of ALSAT-2A Panchromatic and Multispectral Images

Boukerch, Issam; Hadeid, Mohamed; Mahmoudi, R.; Takarli, B.; Hasni, Kamel

doi:10.1007/s12524-014-0380-x

Cited by 4 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These ones are based on the collinearity equation to eliminate the misalignment effect. In (Radhadevi et al 2009;Zhu et al 2012;Boukerch et al 2015;Liu et al, n.d.), the provided ephemeris and attitude data are used to generate an interpolated sensor model. The reference band pixels are then projected to their ground positions by using the obtained model and digital elevation model.…”

Section: Introductionmentioning

confidence: 99%

Multispectral and Panchromatic Registration of Alsat-2 Images Using Dense Vector Matching for Pan-Sharpening Process

Boukerch

Farhi

Karoui

et al. 2018

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

Self Cite

View full text Add to dashboard Cite

The pan-sharpening is a widely used operation in remote sensing image processing, this operation aims at combining an observable high spatial resolution panchromatic image with a multispectral one, to generate an unobservable image with the high spatial resolution of the former and a high spectral resolution of the latter. Generally, papers dealing with this problem omit the geometric part and suppose that these images are perfectly aligned, which is not necessarily the case for the raw imagery, where even the different bands in the multispectral imagery are misaligned. In this paper, new method for multispectral and panchromatic image registration is proposed to deal with the misalignment problem that reduces the pansharpening quality. This method called Dense Vector Matching (DVM) is based on the matching of a whole line-vector or column-vector from a reference band with the corresponding vector in a target band. DVM is applied on real data and has given acceptable results, where the QNR index of the pan-sharpening is better for images after band registration, also the registration error is reduced to sub-pixel using the proposed approach.

show abstract

Section: Introductionmentioning

confidence: 99%

Multispectral and Panchromatic Registration of Alsat-2 Images Using Dense Vector Matching for Pan-Sharpening Process

Boukerch

Farhi

Karoui

et al. 2018

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…This manual approach could produce an accurate result if done very carefully by the expert, but it needs laborious efforts and time consuming. Another approach to correct band co-registration distortion is rigorous sensor modeling which will directly calculate the distortion parameters based on known and validated satellite and imager parameters, both intrinsic and extrinsic ones [6]. However, this approach needs accurate satellite and sensor geometry to produce an accurate result.…”

mentioning

confidence: 99%

Band co-registration modeling of LAPAN-A3/IPB multispectral imager based on satellite attitude

Hakim

Syafrudin

Utama

et al. 2018

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

View the article online for updates and enhancements. Abstract. One of significant geometric distortion on images of LAPAN-A3/IPB multispectral imager is co-registration error between each color channel detector. Band co-registration distortion usually can be corrected by using several approaches, which are manual method, image matching algorithm, or sensor modeling and calibration approach. This paper develops another approach to minimize band co-registration distortion on LAPAN-A3/IPB multispectral image by using supervised modeling of image matching with respect to satellite attitude. Modeling results show that band co-registration error in across-track axis is strongly influenced by yaw angle, while error in along-track axis is fairly influenced by both pitch and roll angle. Accuracy of the models obtained is pretty good, which lies between 1-3 pixels error for each axis of each pair of band co-registration. This mean that the model can be used to correct the distorted images without the need of slower image matching algorithm, nor the laborious effort needed in manual approach and sensor calibration. Since the calculation can be executed in order of seconds, this approach can be used in real time quick-look image processing in ground station or even in satellite on-board image processing.

show abstract

Satellite Attitude Determination Based on Pushbroom Image Band Coregistration

Hakim

Syafrudin

Utama

et al. 2018

2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)

View full text Add to dashboard Cite

Geometry Based Co-Registration of ALSAT-2A Panchromatic and Multispectral Images

Cited by 4 publications

References 12 publications

Multispectral and Panchromatic Registration of Alsat-2 Images Using Dense Vector Matching for Pan-Sharpening Process

Multispectral and Panchromatic Registration of Alsat-2 Images Using Dense Vector Matching for Pan-Sharpening Process

Band co-registration modeling of LAPAN-A3/IPB multispectral imager based on satellite attitude

Satellite Attitude Determination Based on Pushbroom Image Band Coregistration

Contact Info

Product

Resources

About