Automated registration of hyperspectral images

Erives, Hector; Fitzgerald, Glenn J.

doi:10.1117/12.555924

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Images were corrected for lens and filter vignetting and non-uniformities by imaging a 99% Spectralon (Labsphere, Inc., North Sutton, NH) panel near the overflight times (similar solar zenith and azimuth) on the day after each flight under clear sky conditions and then dividing the acquired images by this reference imagery. The images were then co-registered using a phase correlation technique 18 .…”

Section: Hyperspectral Imagery Acquisition and Calibrationmentioning

confidence: 99%

Shadow fraction in spectral mixture analysis of a cotton canopy

et al. 2004

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Hyperspectral imagery is capable of providing detailed spectral reflectance information of agricultural fields for potential use in site-specific management operations. Analysis of these data are complicated by the large number of spectral bands, the many different components or endmembers (e.g. plant and soil), and the presence of shadows. Unlike simple unmixing approaches which compute the fraction of a fixed number of components, multiple endmember spectral mixture analysis (MESMA) also determines which components are present in each pixel. This study compared whether using different shadow endmembers (EM) in a 4-EM model (sunlit green leaf, sunlit soil, shadowed leaf, shadowed soil) would improve estimates of scene components compared to a 3-EM model (sunlit green leaf, sunlit soil, photometric shade). Results revealed that correlations with percent cover and height were improved when shadow or shade endmembers were included for both models compared to the green leaf fraction alone. The 3-EM model was superior for developing a direct relationship for estimating cover and height but was not able to estimate SPAD or chlorophyll a. The 4-EM model showed the best results for SPAD and chlorophyll a, with r 2 values of 0.84 and 0.77, respectively.

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Section: Hyperspectral Imagery Acquisition and Calibrationmentioning

confidence: 99%

Shadow fraction in spectral mixture analysis of a cotton canopy

et al. 2004

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“…In this subproblem, we need to estimate L i from the low-rank tensor Z i . By ignoring the variables irrelevant to L i in (7), we can get the subproblem:…”

Section: ) Low-rank Tensor Regularization Termmentioning

confidence: 99%

“…T HANKS to the advancements in imaging technology, hyperspectral image (HSI) is capable of providing abundant information regarding the wavelengths beyond the visible spectrum and have a wide range of applications including medical diagnosis [1], [2], [3], geothermal exploration [4], [5], [6], agriculture [7], [8], [9]. Unfortunately, different types of noise including stripes, deadlines, impulse noise, and Gaussian noise will be inevitably introduced in the hyperspectral imaging process, which considerably damages the image quality and limits further applications including image classification [10], [11] and target detection [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization

Zhou¹,

Su²,

Li³

2021

Preprint

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Recently, the low-rank property of different components extracted from the image has been considered in many hyperspectral image denoising methods. However, these methods usually unfold the 3D tensor to 2D matrix or 1D vector to exploit the prior information, such as nonlocal spatial selfsimilarity (NSS) and global spectral correlation (GSC), which break the intrinsic structure correlation of hyperspectral image (HSI) and thus lead to poor restoration quality. In addition, most of them suffer from heavy computational burden issues due to the involvement of singular value decomposition operation on matrix and tensor in the original high-dimensionality space of HSI. We employ subspace representation and the weighted low-rank tensor regularization (SWLRTR) into the model to remove the mixed noise in the hyperspectral image. Specifically, to employ the GSC among spectral bands, the noisy HSI is projected into a lowdimensional subspace which simplified calculation. After that, a weighted low-rank tensor regularization term is introduced to characterize the priors in the reduced image subspace. Moreover, we design an algorithm based on alternating minimization to solve the nonconvex problem. Experiments on simulated and real datasets demonstrate that the SWLRTR method performs better than other hyperspectral denoising methods quantitatively and visually.

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Automated registration of hyperspectral images

Cited by 2 publications

References 7 publications

Shadow fraction in spectral mixture analysis of a cotton canopy

Shadow fraction in spectral mixture analysis of a cotton canopy

Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization

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