Comparative study for image registration techniques of remote sensing images

Ezzeldeen, R.M.; Ramadan, Hassan H.; Nazmy, Taymoor; Yehia, M.; Abdelwahab, Mohamed

doi:10.1016/j.ejrs.2010.07.004

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…When introducing artificial rotations, they found a RMSE of about 2·0 image pixels for ±5° rotation angles. Ezzeldeen et al (2010), comparing the performances of several different image registration techniques on Landsat TM image pairs, found average values of RMSE from sub‐pixel values to a few image pixels, depending on scene content and the algorithm used. Their results concluded that the more stable of the three techniques tested produced the largest RMSE.…”

Section: Resultsmentioning

confidence: 99%

Automatic Co‐Registration of Satellite Time Series

Gianinetto

2012

The Photogrammetric Record

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In modern remote sensing applications the use of automatic image processing codes is becoming increasingly common. When dealing with satellite time series or multi‐source data, image co‐registration is a time‐consuming, but necessary, pre‐processing step. This study shows how a medium resolution satellite multitemporal dataset can be effectively processed using automatic data processing. Results on real and transformed ASTER data showed that, without human interaction, the automatic ground control points extraction (AGE) technique, developed at Politecnico di Milano, is able to obtain accuracies generally sufficient for practical applications and similar to those reported in other studies.

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Section: Resultsmentioning

confidence: 99%

Automatic Co‐Registration of Satellite Time Series

Gianinetto

2012

The Photogrammetric Record

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“…The registration of multimodal images is common in medical 27–30 and remote sensing fields 31–33 . In these application scenarios, sensors are usually static and do not require strict synchronization in image acquisition.…”

Section: Registration Methodsmentioning

confidence: 99%

“…The registration of multimodal images is common in medical [27][28][29][30] and remote sensing fields. [31][32][33] In these application scenarios, sensors are usually static and do not require strict synchronization in image acquisition. Due to the specificity of the metro environment, we cannot build sensing units in each location to passively acquire images of different scenes.…”

Section: Synchronous Acquisitionmentioning

confidence: 99%

MRSI: A multimodal proximity remote sensing data set for environment perception in rail transit

Chen

Zhu²,

et al. 2021

Int J of Intelligent Sys

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Rail transit is becoming a major mode of rapid urban and intercity passenger and freight transportation, and its safe operation is of great significance in safeguarding people's lives and properties and maintaining social stability. The current scheme of manual hazard monitoring in rail transit still remains potential safety risks. Accurate rail scene understanding is an essential step towards a smart train. Limited by the closeness of railway scenes, not much research has been conducted on the perception and understanding of rail transit. In view of the above, we propose multimodal remote sensing image (MRSI), the first multimodal proximity remote sensing data set for rail scene understanding. MRSI consists of 27k images collected from freight rail and metro following the pixel and box annotations labeled and checked manually. We used a variety of sensing devices mounted on locomotives to record track scenes under different lighting and weather conditions, including straight, curve, and fork during daytime, dusk, and nighttime, as well as under rainy days. We also include an additional infrared thermometer in the metro environment, propose a new image registration method after synchronous acquisition, and thus construct MRSI combining spatial and radiometric properties. With this data set, we can achieve segmentation of the track area and recognition of obstacles by sensing the environment in front of the train, which lead to rail scene understanding. MRSI is publicly available at https://zenodo.org/record/5732905#.YaPIpsdBwdU.

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“…The misalignment could be due to the change in viewpoint, change in the sensor position, the movement of the object or its deformation, and the illumination. [1] Recently many applications and data have been using image registration. In case of remote sensing imaging registration is very essential in analysis of remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Satellite Image Registration based on SURF and MI

Krishna¹,

Ramkumar²

2015

IJERT

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Registration of satellite imagery is key step for remote sensing applications like global change detection, image fusion, and feature classification. Manual registration is very time consuming and repetitive, an automatic way for image registration is needed. In this paper such a method for image registration is proposed which is fully automatic and computationally efficient unlike global registration in which control points are selected manually. There is a preregistration process and a fine-tuning process. The first stage includes feature selection and description using SURF and an outlier removal procedure using RANSAC. This gets the optimizer in the fine-tuning process a near optimal solution. Next, the fine-tuning process is implemented by the maximization of mutual information. The proposed scheme is tested on various remote sensing images taken at different situations (multispectral, multisensor, and multitemporal) with the affine transformation model. It is demonstrated experimentally that proposed scheme is fully automatic and much efficient than the global registration. SURF is mostly used algorithm as it is the fastest descriptor. This paper shows that by increasing the matching points, image registration can be accurately done.

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Comparative study for image registration techniques of remote sensing images

Cited by 12 publications

References 11 publications

Automatic Co‐Registration of Satellite Time Series

Automatic Co‐Registration of Satellite Time Series

MRSI: A multimodal proximity remote sensing data set for environment perception in rail transit

Satellite Image Registration based on SURF and MI

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