Remote Sensing Image Registration Techniques: A Survey

Dawn, Suma; Saxena, Vikas; Sharma, Bhudev

doi:10.1007/978-3-642-13681-8_13

Cited by 107 publications

(62 citation statements)

References 55 publications

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“…The calculated abundances,á p,c , are found using Equation (18), and the closeness of fit, R 2 c , is found using Equation (19), whereā c represents the mean unmixing fraction of class c. …”

Section: Comparison Of Spectral Unmixing Results and Amrdmentioning

confidence: 99%

“…Common approaches include feature matching and intensity correlation, with comparisons made in both the spatial and frequency domains, while post registration resampling and alignment of data are carried out with affine transforms or warping [17]. Remote sensing image registration has often relied on feature matching methods and warping [18,19]. It should be noted that image registration for RSRD is challenging due to highly dissimilar spatial resolution, the need for sub-pixel registration accuracy, and the use of different imaging sensors.…”

Section: Spatial Alignmentmentioning

confidence: 99%

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Application of Abundance Map Reference Data for Spectral Unmixing

2017

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Reference data ("ground truth") maps have traditionally been used to assess the accuracy of classification algorithms. These maps typically classify pixels or areas of imagery as belonging to a finite number of ground cover classes, but do not include sub-pixel abundance estimates; therefore, they are not sufficiently detailed to directly assess the performance of spectral unmixing algorithms. Our research aims to efficiently generate, validate, and apply abundance map reference data (AMRD) to airborne remote sensing scenes. Scene-wide AMRD for this study were generated using the remotely sensed reference data (RSRD) technique, which spatially aggregates classification or unmixing results from fine scale imagery (e.g., 1-m GSD) to co-located coarse scale imagery (e.g., 10-m GSD or larger). Validation of the accuracy of these methods was previously performed for generic 10 m × 10 m coarse scale imagery, resulting in AMRD with known accuracy. The purpose of this paper was to apply this previously validated AMRD to specific examples of airborne coarse scale imagery. Application of AMRD involved three main parts: (1) spatial alignment of coarse and fine scale imagery; (2) aggregation of fine scale abundances to produce coarse scale imagery specific AMRD; and (3) demonstration of comparisons between coarse scale unmixing abundances and AMRD. Spatial alignment was performed using our new scene-wide spectral comparison (SWSC) algorithm, which aligned imagery with accuracy approaching the distance of a single fine scale pixel. We compared simple rectangular aggregation to coarse sensor point-spread function (PSF) aggregation, and found that PSF returned lower error, but that rectangular aggregation more accurately estimated true AMRD at ground level. We demonstrated various metrics for comparing unmixing results to AMRD, including several new techniques which adjust for known error in the reference data itself. These metrics indicated that fully constrained linear unmixing of AVIRIS imagery across all three scenes returned an average error of 10.83% per class and pixel. Our reference data research has demonstrated a viable methodology to efficiently generate, validate, and apply AMRD to specific examples of airborne remote sensing imagery, thereby enabling direct quantitative assessment of spectral unmixing performance.

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Section: Comparison Of Spectral Unmixing Results and Amrdmentioning

confidence: 99%

Section: Spatial Alignmentmentioning

confidence: 99%

Application of Abundance Map Reference Data for Spectral Unmixing

2017

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“…It is widely used in various applications of computer vision and remote sensing, such as image registration and fusion, change detection, and environment monitoring. Automatic image matching technology has been widely studied in the fields of computer vision and remote sensing in the past decades [1][2][3][4]. Unlike those in computer vision applications, the images in remote sensing (multi-temporal and multi-source images) are usually affected by complex background variations, such as noise caused by cloud and haze weather conditions and land cover changes caused by human construction activities and disasters (earthquakes and floods) [5].…”

Section: Introductionmentioning

confidence: 99%

Matching of Remote Sensing Images with Complex Background Variations via Siamese Convolutional Neural Network

Chen

et al. 2018

Remote Sensing

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Abstract:Feature-based matching methods have been widely used in remote sensing image matching given their capability to achieve excellent performance despite image geometric and radiometric distortions. However, most of the feature-based methods are unreliable for complex background variations, because the gradient or other image grayscale information used to construct the feature descriptor is sensitive to image background variations. Recently, deep learning-based methods have been proven suitable for high-level feature representation and comparison in image matching. Inspired by the progresses made in deep learning, a new technical framework for remote sensing image matching based on the Siamese convolutional neural network is presented in this paper. First, a Siamese-type network architecture is designed to simultaneously learn the features and the corresponding similarity metric from labeled training examples of matching and non-matching true-color patch pairs. In the proposed network, two streams of convolutional and pooling layers sharing identical weights are arranged without the manually designed features. The number of convolutional layers is determined based on the factors that affect image matching. The sigmoid function is employed to compute the matching and non-matching probabilities in the output layer. Second, a gridding sub-pixel Harris algorithm is used to obtain the accurate localization of candidate matches. Third, a Gaussian pyramid coupling quadtree is adopted to gradually narrow down the searching space of the candidate matches, and multiscale patches are compared synchronously. Subsequently, a similarity measure based on the output of the sigmoid is adopted to find the initial matches. Finally, the random sample consensus algorithm and the whole-to-local quadratic polynomial constraints are used to remove false matches. In the experiments, different types of satellite datasets, such as ZY3, GF1, IKONOS, and Google Earth images, with complex background variations are used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method, which can significantly improve the matching performance of multi-temporal remote sensing images with complex background variations, is better than the state-of-the-art matching methods. In our experiments, the proposed method obtained a large number of evenly distributed matches (at least 10 times more than other methods) and achieved a high accuracy (less than 1 pixel in terms of root mean square error).

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“…Image registration is the process of geometrically-aligning two or more images of the same scene taken from different viewpoints, at different times or by different sensors, and the main objective of it is to find the most appropriate transformation parameters between the images [1][2][3][4]. It has been widely used in the field of remote sensing, including image fusion, change detection [5][6][7], disaster analysis [8], semantic labelling of images [9] and mapping sciences [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

Shi

Jiang

2016

Remote Sensing

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Abstract:Image registration is an essential step in the process of image fusion, environment surveillance and change detection. Finding correct feature matches during the registration process proves to be difficult, especially for remote sensing images with large background variations (e.g., images taken pre and post an earthquake or flood). Traditional registration methods based on local intensity probably cannot maintain steady performances, as differences are significant in the same area of the corresponding images, and ground control points are not always available in many disaster images. In this paper, an automatic image registration method based on the line segments on the main shape contours (e.g., coastal lines, long roads and mountain ridges) is proposed for remote sensing images with large background variations because the main shape contours can hold relatively more invariant information. First, a line segment detector called EDLines (Edge Drawing Lines), which was proposed by Akinlar et al. in 2011, is used to extract line segments from two corresponding images, and a line validation step is performed to remove meaningless and fragmented line segments. Then, a novel line segment descriptor with a new histogram binning strategy, which is robust to global geometrical distortions, is generated for each line segment based on the geometrical relationships,including both the locations and orientations of theremaining line segments relative to it. As a result of the invariance of the main shape contours, correct line segment matches will have similar descriptors and can be obtained by cross-matching among the descriptors. Finally, a spatial consistency measure is used to remove incorrect matches, and transformation parameters between the reference and sensed images can be figured out. Experiments with images from different types of satellite datasets, such as Landsat7, QuickBird, WorldView, and so on, demonstrate that the proposed algorithm is automatic, fast (4 ms faster than the second fastest method, i.e., the rotation-and scale-invariant shape context) and can achieve a recall of 79.7%, a precision of 89.1% and a root mean square error (RMSE) of 1.0 pixels on average for remote sensing images with large background variations.

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Remote Sensing Image Registration Techniques: A Survey

Cited by 107 publications

References 55 publications

Application of Abundance Map Reference Data for Spectral Unmixing

Application of Abundance Map Reference Data for Spectral Unmixing

Matching of Remote Sensing Images with Complex Background Variations via Siamese Convolutional Neural Network

Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

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