Accurate geometric correction of ATSR images

Shin, Dongseok; Pollard, J.K.; Müller, Jan-Peter

doi:10.1109/36.602542

Cited by 46 publications

(3 citation statements)

References 13 publications

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“…General line segments (Maitre and Wu, 1987;Wang and Chen, 1997;Moss and Hancock, 2002) coastal lines (Shin et al, 1997), object contours (Li et al, 1995;Dai and Khorram, 1997;Govindu et al, 1998), roads (Li et al, 1992), or elongated anatomic structures (Vujovic and Brzakovic, 1997) in medical imaging exemplify representation of the line features.…”

Section: Feature Based Methodsmentioning

confidence: 99%

Image Registration Methods: A Short Review

Gupta¹,

Chakarvarti²,

Zaheerudin³

2013

AJAC

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The purpose of this paper is to provide a review of classic as well as recent image registration methods. Image registration method aims to align two or more images of the same scene taken at different times, with different instruments, from different viewpoints. In this process two images (the reference and sensed images) are geometrically aligned. The reviewed approaches are classified according to four basic steps of the image registration procedure: feature detection, control points matching, a design of the mapping function, and image transformation and according to their nature (area based and feature-based). The paper also has an objective to provide a comprehensive study of different image registration methods, regardless of particular application areas.

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Section: Feature Based Methodsmentioning

confidence: 99%

Image Registration Methods: A Short Review

Gupta¹,

Chakarvarti²,

Zaheerudin³

2013

AJAC

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“…As can be seen, the normalization results will fundamentally affect the invariance of the representation. Over the years, the design of such a normalization approach has been pursued by many researchers in different tasks such as geometric correction [81,82] and image registration [83,84]. One of the simplest methods is based on geometric moments, which obtain translation, scale, and rotation invariance by evaluating/eliminating the centroid, scaling factor, and principal axis of the image, all based on low-order geometric moments.…”

Section: Robustness/invariance Optimizationmentioning

confidence: 99%

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

Qi,

Zhang,

Wang

et al. 2021

Preprint

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Image representation is an important topic in computer vision and pattern recognition. It plays a fundamental role in a range of applications towards understanding visual contents. Moment-based image representation has been reported to be effective in satisfying the core conditions of semantic description due to its beneficial mathematical properties, especially geometric invariance and independence. This paper presents a comprehensive survey of the orthogonal moments for image representation, covering recent advances in fast/accurate calculation, robustness/invariance optimization, and definition extension. We also create a software package for a variety of widely-used orthogonal moments and evaluate such methods in a same base. The presented theory analysis, software implementation, and evaluation results can support the community, particularly in developing novel techniques and promoting realworld applications.

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“…These features can be points (e.g., corners and intersections), lines (e.g., roads and rivers), or regions (e.g., fields and lakes). Current feature-based techniques pay much attention to point features [14]- [19], [21], while line features are scarcely discussed [11], [23]. Although feature-based techniques that focus on regions have been studied for a long time, these techniques still remain undeveloped [9], [10], [12], [13], [20], [24].…”

Section: Introductionmentioning

confidence: 99%

A Stable Land Cover Patches Method for Automatic Registration of Multitemporal Remote Sensing Images

Cao

Zhu

Pan

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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We propose a stable land cover patches method (SLCPM) for the automatic registration of multitemporal remote sensing images. Our method takes advantage of multispectral features as well as stable and widespread land cover patches in remote sensing images. We tested our method using optical satellite images through four different experiments. In the first and second experiments, we tried to register images acquired on different days but by the same sensor. In the third experiment, we tried to register images acquired on different days and by different sensors (which means different spectral resolutions were also considered). In the fourth experiment, we tried to register images with different spatial resolutions acquired on different dates and by different sensors. Three indices were used in our paper for quality evaluation: overall Root Mean Square Error (RMS ), Root Mean Square Error calculated by the leave-one-out method (RMS ), and a statistical evaluation of the goodness of control point (CP) distribution across the image (S ).Results showed SLCPM could generate sufficient, accurate and well distributed CP pairs. We further compared our method with two other popular automatic image registration methods-scale invariant feature transform (SIFT) and a contour-based approach. The contour-based approach could hardly generate any CPs in all the experiments, while SIFT performed very well in the first three experiments both in accuracy and distribution of CPs but was ineffective in the most complex (i.e., last) experiment, due to the lack of correct CPs.Index Terms-Boundary moment invariants, multitemporal image registration, opening operation, stable land cover patches.

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Accurate geometric correction of ATSR images

Cited by 46 publications

References 13 publications

Image Registration Methods: A Short Review

Image Registration Methods: A Short Review

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

A Stable Land Cover Patches Method for Automatic Registration of Multitemporal Remote Sensing Images

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