Status and application of advanced airborne hyperspectral imaging technology: A review

Jia, Jianxin; Wang, Yueming; Chen, Jinsong; Guo, Ruyan; Shu, Rong; Wang, Jianyu

doi:10.1016/j.infrared.2019.103115

Cited by 106 publications

(58 citation statements)

References 131 publications

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“…Hyperspectral remote sensing combines imaging technology and spectroscopy technology to obtain continuous and narrow-band image data with high spectral resolution [1], which improves the ability to monitor the Earth's systems and human activities [2,3]. However, the high-dimensional data obtained by the hyperspectral sensor are challenging to analyse and apply [4,5].…”

Section: Introductionmentioning

confidence: 99%

Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

et al. 2021

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Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the original data perfectly. However, the conventional KMNF noise estimation (KMNF-NE) uses the local regression residual of neighbourhood pixels, which depends heavily on spatial information. Due to the limited spatial resolution, there are many mixed pixels in HSI, making KMNF-NE unreliable for noise estimation and leading to poor performance in KMNF for classification on HSIs with low spatial resolution. In order to overcome this problem, a mixed noise estimation model (MNEM) is proposed in this paper for optimized KMNF (OP-KMNF). The MNEM adopts the sequential and linear combination of the Gaussian prior denoising model, median filter, and Sobel operator to estimate noise. It retains more details and edge features, making it more suitable for noise estimation in KMNF. Experiments using several HSI datasets with different spatial and spectral resolutions are conducted. The results show that, compared with some other DR methods, the improvement of OP-KMNF in average classification accuracy is up to 4%. To improve the efficiency, the OP-KMNF was implemented on graphics processing units (GPU) and sped up by about 60× compared to the central processing unit (CPU) implementation. The outcome demonstrates the significant performance of OP-KMNF in terms of classification ability and execution efficiency.

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Section: Introductionmentioning

confidence: 99%

Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

et al. 2021

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“…Their focus is to acquire a three-dimensional datacube from a scene, with two spatial and one spectral dimension. Datacubes can be encapsulated in four different ways depending on how the data are captured [ 7 ]: Whiskbroom: A point of information is captured over the whole spectral information at once [ 8 ]. Scanning point-by-point both spatial dimensions and mapping each capture to a 2D detector would yield a complete datacube; Pushbroom: A slit image of the scene containing spatial information along one axis and spectral information along the other is obtained at once [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Whiskbroom: A point of information is captured over the whole spectral information at once [ 8 ]. Scanning point-by-point both spatial dimensions and mapping each capture to a 2D detector would yield a complete datacube;…”

Section: Introductionmentioning

confidence: 99%

Coded Aperture Hyperspectral Image Reconstruction

García-Sánchez

Fresnedo

González-Coma

et al. 2021

Sensors

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In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from their compressive measurements. In particular, we considered and compared four different type of estimation algorithms: OMP, GPSR, LASSO, and IST. Furthermore, the large dimensions of hyperspectral images required the implementation of a practical block CASSI model to reconstruct the images with an acceptable delay and affordable computational cost. In order to consider the particularities of the block model and the dispersive effects in the CASSI-like sensing procedure, the problem was reformulated, as well as the construction of the variables involved. For this practical CASSI setup, we evaluated the performance of the overall system by considering the aforementioned algorithms and the different factors that impacted the reconstruction procedure. Finally, the obtained results were analyzed and discussed from a practical perspective.

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“…Due to the high spectral resolution of the HSI, HSI can be widely used in environmental surveillance, military surveillance, medical detection, and agricultural planning, etc. [1]- [10]. However, higher spectral resolution means larger volume, which will greatly increase the burden of transmission and storage.…”

Section: Introductionmentioning

confidence: 99%

T-Hy-Demosaicing: Hyperspectral Reconstruction Via Tensor Subspace Representation Under Orthogonal Transformation

Huang

Lin

et al. 2021

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

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This paper aims to solve the problem of the hyperspectral imagery (HSI) demosaicing under a novel subsampling hyperspectral sensing strategy. The existing method utilizes the periodic structure of sub-sampling to estimate a fixed subspace in matrix form from the measurement result, which reduces the representation ability of the subspace in iterations and destroys the intrinsic structure of the tensor. To overcome these drawbacks, we propose a tensor-based HSI demosaicing (T-Hydemosaicing) model with tensor subspace representation, which takes the low-tubal-rankness and the nonlocal self-similarity into account. In particular, we suggest a tensor singular value decomposition based on orthogonal transformation (Tran-based t-SVD) to learn the tensor subspace that possesses a more powerful representation ability. In addition, we develop an effective algorithm to solve the proposed non-convex model under the framework of the proximal alternating minimization (PAM) algorithm. Experiments conducted on simulated datasets illustrate that the proposed method outperforms other comparative methods in both visual and quantitative terms.

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Status and application of advanced airborne hyperspectral imaging technology: A review

Cited by 106 publications

References 131 publications

Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

Coded Aperture Hyperspectral Image Reconstruction

T-Hy-Demosaicing: Hyperspectral Reconstruction Via Tensor Subspace Representation Under Orthogonal Transformation

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