A Vector SIFT Detector for Interest Point Detection in Hyperspectral Imagery

Dorado-Munoz, Leidy P.; Vélez-Reyes, Miguel; Mukherjee, Amit; Roysam, Badrinath

doi:10.1109/tgrs.2012.2191791

Cited by 26 publications

(26 citation statements)

References 23 publications

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“…Among the proposed approaches, one may cite the extension of SIFT for multispectral [19,20] and hyperspectral data [22,23], as well as approaches that optimize the SIFT parameters in order to maximize the number of correspondences [21].…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the image descriptors are not computed over the entire hypercube [23] or the entire projection space [22], but they are computed at severally bands of the hyperspectral data. This is of significant importance if one takes into account that feature point descriptors are already high-dimensional vectors while the vector SIFT formulation multiplies their complexity by the number of the spectral bands.…”

Section: Contributionsmentioning

confidence: 99%

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Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data

Vakalopoulou

Καράντζαλος

2014

Remote Sensing

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Frame hyperspectral sensors, in contrast to push-broom or line-scanning ones, produce hyperspectral datasets with, in general, better geometry but with unregistered spectral bands. Being acquired at different instances and due to platform motion and movements (UAVs, aircrafts, etc.), every spectral band is displaced and acquired with a different geometry. The automatic and accurate registration of hyperspectral datasets from frame sensors remains a challenge. Powerful local feature descriptors when computed over the spectrum fail to extract enough correspondences and successfully complete the registration procedure. To this end, we propose a generic and automated framework which decomposes the problem and enables the efficient computation of a sufficient amount of accurate correspondences over the given spectrum, without using any ancillary data (e.g., from GPS/IMU). First, the spectral bands are divided in spectral groups according to their wavelength. The spectral borders of each group are not strict and their formulation allows certain overlaps. The spectral variance and proximity determine the applicability of every spectral band to act as a reference during the registration procedure. The proposed decomposition allows the descriptor and the robust estimation process to deliver numerous inliers. The search space of possible solutions has been effectively narrowed by sorting and selecting the optimal spectral bands which under an unsupervised manner can quickly recover hypercube's geometry. The developed approach has been qualitatively and quantitatively evaluated with six different datasets obtained by frame sensors onboard aerial platforms and UAVs. Experimental results appear promising.Remote Sens. 2014, 6 3410

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Section: Related Workmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data

Vakalopoulou

Καράντζαλος

2014

Remote Sensing

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“…FED schemes are characterized by their faster computation, ease of implementation, and higher accuracy than AOS approaches. Finally, HSI-KAZE uses a descriptor consisting in two parts: the M-SURF descriptor as in the original KAZE and the spectral signature of the keypoint (Figure 7, lines [24][25][26][27][28][29]. Thus, thanks to the use of spectral information, a more robust matching is performed.…”

Section: Keypoint Detection and Descriptionmentioning

confidence: 99%

“…Ref. [24] presented a method where the scale space is created using a nonlinear diffusion equation taking into account the spectral information. The keypoint localization consists in comparing each pixel vector to its neighbourhood according to their spectral signature.…”

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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“…The registration method in this article is based on a third-order polynomial transformation model and the SIFT features to deal with both of the misalignments above. Instead of SIFT features, the method can readily use other features such as SS-SIFT [27], GA-ORB [28], KAZE [29], Vector-SIFT [30], modified SIFT [31] or other feature detector-descriptor methods that can provide a reliable set of control points.…”

Section: Introductionmentioning

confidence: 99%

Configuration and Registration of Multi-Camera Spectral Image Database of Icon Paintings

Mirhashemi

2019

Computation

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At the cost of added complexity and time, hyperspectral imaging provides a more accurate measure of the scene’s irradiance compared to an RGB camera. Several camera designs with more than three channels have been proposed to improve the accuracy. The accuracy is often evaluated based on the estimation quality of the spectral data. Currently, such evaluations are carried out with either simulated data or color charts to relax the spatial registration requirement between the images. To overcome this limitation, this article presents an accurately registered image database of six icon paintings captured with five cameras with different number of channels, ranging from three (RGB) to more than a hundred (hyperspectral camera). Icons are challenging topics because they have complex surfaces that reflect light specularly with a high dynamic range. Two contributions are proposed to tackle this challenge. First, an imaging configuration is carefully arranged to control the specular reflection, confine the dynamic range, and provide a consistent signal-to-noise ratio for all the camera channels. Second, a multi-camera, feature-based registration method is proposed with an iterative outlier removal phase that improves the convergence and the accuracy of the process. The method was tested against three other approaches with different features or registration models.

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A Vector SIFT Detector for Interest Point Detection in Hyperspectral Imagery

Cited by 26 publications

References 23 publications

Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data

Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data

Alignment of Hyperspectral Images Using KAZE Features

Configuration and Registration of Multi-Camera Spectral Image Database of Icon Paintings

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