Multi-spectral Satellite Image Registration Using Scale-Restricted SURF

Teke, Mustafa; Temizel, Alptekin

doi:10.1109/icpr.2010.565

Cited by 45 publications

(27 citation statements)

References 7 publications

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“…A similar approach was followed by [20], who proposed a descriptor vector with 4 orientation bins instead of 8. These restrictions were also adapted to SURF in [21]. Moreover, Ref.…”

Section: Introductionmentioning

confidence: 99%

Alignment of Hyperspectral Images Using KAZE Features

2018

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Abstract:Image registration is a common operation in any type of image processing, specially in remote sensing images. Since the publication of the scale-invariant feature transform (SIFT) method, several algorithms based on feature detection have been proposed. In particular, KAZE builds the scale space using a nonlinear diffusion filter instead of Gaussian filters. Nonlinear diffusion filtering allows applying a controlled blur while the important structures of the image are preserved. Hyperspectral images contain a large amount of spatial and spectral information that can be used to perform a more accurate registration. This article presents HSI-KAZE, a method to register hyperspectral remote sensing images based on KAZE but considering the spectral information. The proposed method combines the information of a set of preselected bands, and it adapts the keypoint descriptor and the matching stage to take into account the spectral information. The method is adequate to register images in extreme situations in which the scale between them is very different. The effectiveness of the proposed algorithm has been tested on real images taken on different dates, and presenting different types of changes. The experimental results show that the method is robust achieving image registrations with scales of up to 24.0×.

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“…A similar approach was followed by [20], who proposed a descriptor vector with 4 orientation bins instead of 8. These restrictions were also adapted to SURF in [21]. Moreover, Ref.…”

Section: Introductionmentioning

confidence: 99%

Alignment of Hyperspectral Images Using KAZE Features

2018

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“…UAV multispectral images usually have multiple bands, and the selected band used in feature matching has an obvious influence on the matching result directly [25,26]. As the ground targets have various reflection characteristics, the environmental conditions, such as the atmosphere and the relative positions of the sun, the targets, and the sensor may also change.…”

Section: Related Researchmentioning

confidence: 99%

A Simplified Method for UAV Multispectral Images Mosaicking

et al. 2017

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This paper presents a method for mosaicking unmanned aerial vehicle (UAV) multispectral images. The main purpose of the proposed method is to reduce spatial distortion in the mosaicking process and increase robustness and the speed of the operation. Most UAV multispectral images have multiple bands, and in every band, ground targets have a variety of reflection characteristics that will result in diverse feature quality for feature matching. In this research, an information entropy-based evaluation method is used to select the optimal band for feature matching among the UAV images. To produce more robust matching results for the following alignment step, the evaluation method takes the contrast and spatial distribution of the feature points into consideration at the same time. In most common image mosaicking processes, the digital orthophoto map (DOM) is generated to achieve maximum spatial accuracy. During this process, the original image data will experience considerable irregular resampling, and the process is also unstable in some circumstances. The alignment step uses a simplified projection model that treats the ground as planar is provided, by which the alignment parameters are applied directly to the images instead of generating 3D points, to avoid irregular resampling and unstable 3D reconstruction. The proposed method is proved to be more efficient and accurate and has lower spectral distortion than state-of-the-art mosaicking software.

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“…As this descriptor is quite innovative (2008), it has been scarcely applied to remote sensing images. On the other hand, the technical literature reports some scientific contributions [Teke and Temizel, 2010;Bouchiha and Besbes, 2013] where the method was tested on image pairs and compared to other similar operators (SIFT [Lowe, 2004], PCA-SIFT [Ke and Sukthankar, 2004], GLOH , see for a detailed review of image matching results), obtaining sub-pixel precision and robustness against scale variation, translation, rotation and changes in brightness values. SURF relies on a Hessian matrix-based measure for the detector and the distribution of the first-order Haar wavelet responses for the descriptor [Bay et al, 2008].…”

Section: Automated Measurement Of Corresponding Featuresmentioning

confidence: 99%

Automatic Co-registration of Satellite Time Series via Least Squares Adjustment

Barazzetti

Scaioni

Gianinetto

2014

European Journal of Remote Sensing

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Image-to-image co-registration is a fundamental task for data processing of satellite time series. This paper presents a new multi-image co-registration algorithm that simultaneously uses multi-image corresponding points in the whole multi-temporal sequence. Image co-registration parameters are then computed on the basis of a global adjustment. The implemented algorithm provides sub-pixel accuracy similar to that achievable with interactive measurements, but it is also able to register images which do not directly share corresponding features with the reference. Results for a (i) synthetic dataset and a (ii) real complex multi-temporal series made up of 13 Landsat images collected over a period of 30 years are illustrated and discussed. The obtained results showed that the implemented algorithm is atmospheric resistant and quite robust against land cover changes, cloud cover, and snow.

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Multi-spectral Satellite Image Registration Using Scale-Restricted SURF

Cited by 45 publications

References 7 publications

Alignment of Hyperspectral Images Using KAZE Features

Alignment of Hyperspectral Images Using KAZE Features

A Simplified Method for UAV Multispectral Images Mosaicking

Automatic Co-registration of Satellite Time Series via Least Squares Adjustment

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