High spatial resolution hyperspectral camera based on exponentially variable filter

Renhorn, Ingmar; Axelsson, Linnéa

doi:10.1117/1.oe.58.10.103106

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…When the camera and the observed surface are static, each point on the object will thus be observed in a specific wavelength (Renhorn et al, 2016, Ahlberg et al, 2017. By rotating the camera, it can be used as an imaging spectrometer, as described in (Renhorn, Axelsson, 2019).…”

Section: Hyperspectral Imagermentioning

confidence: 99%

The Detection of Concealed Targets in Woodland Areas Using Hyperspectral Imagery

González

Shimoni²,

Plaza

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Recent innovations in microelectronic and semiconductor technology enable the creation of smaller and economical hyperspectral cameras. A filter combined camera with advanced scanning module is a game changer that extends the application of miniature hyperspectral imagers to many security domains. This work presents the assessment of the imager L4 from Glana Sensors to detect concealed targets in woodland areas. Several target detection methods were applied to a collection of scenes acquired under various illumination conditions and containing different materials. The potential and limitations of this new imaging device in the context of difficult target detection in forested area are evaluated and discussed.

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Section: Hyperspectral Imagermentioning

confidence: 99%

The Detection of Concealed Targets in Woodland Areas Using Hyperspectral Imagery

González

Shimoni²,

Plaza

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In addition, the latest technological advances are introducing hyperspectral cameras with a higher spectrum and spatial resolution to the market. From the perspective of onboard processing, communication, and storage, all of these make efficient data processing more challenging [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing

et al. 2021

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The high complexity of the reconstruction algorithm is the main bottleneck of the hyperspectral image (HSI) compression technology based on compressed sensing. Compressed sensing technology is an important tool for retrieving the maximum number of HSI scenes on the ground. However, the complexity of the compressed sensing algorithm is limited by the energy and hardware of spaceborne equipment. Aiming at the high complexity of compressed sensing reconstruction algorithm and low reconstruction accuracy, an equivalent model of the invertible transformation is theoretically derived by us in the paper, which can convert the complex invertible projection training model into the coupled dictionary training model. Besides, aiming at the invertible projection training model, the most competitive task-driven invertible projection matrix learning algorithm (TIPML) is proposed. In TIPML, we don’t need to directly train the complex invertible projection model, but indirectly train the invertible projection model through the training of the coupled dictionary. In order to improve the accuracy of reconstructed data, in the paper, the singular value transformation is proposed. It has been verified that the concentration of the dictionary is increased and that the expressive ability of the dictionary has not been reduced by the transformation. Besides, two-loop iterative training is established to improve the accuracy of data reconstruction. Experiments show that, compared with the traditional compressed sensing algorithm, the compressed sensing algorithm based on TIPML has higher reconstruction accuracy, and the reconstruction time is shortened by more than a hundred times. It is foreseeable that the TIPML algorithm will have a huge application prospect in the field of HSI compression.

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“…This technique is complicated, relative to our use of LC shutters, given that it makes use of broadband laser emission. Filter array techniques can also be noted [29]- [33], Innovative Filter Solutions for Hyperspectral Imaging, (2016), Optical Components. Here, a mosaic of filters are arranged consecutively before a photometer.…”

Section: Introductionmentioning

confidence: 99%

Single-Point Detection Architecture via Liquid Crystal Modulation for Hyperspectral Imaging Systems

Brodie

Collier

2020

IEEE Access

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Hyperspectral imaging (HSI) architectures can acquire one-dimension of spatial information and one-dimension of spectral information on a two-dimensional image sensor for an image, such as in the traditional line-scan HSI architecture. However, development of HSI architectures for multiple spatial dimensions is challenging as there is not a third dimension on a two-dimensional image sensor on which to store spectral information. The presented work introduces a snapshot HSI architecture to alleviate this issue. The snapshot HSI architecture incorporates single-point detection via liquid crystal modulation and a single photodiode. Mixing of hyperspectral data is expressed as intermodulation frequency products within the Fourier-domain. Spatial information can be recorded through spatial frequencies and spectral information can be recorded through spectral frequencies. Such modulation is achieved through liquid crystal spatial and spectral arrays of an image beam. The spatial and spectral modulation frequencies form intermodulation frequency products that are recorded on the single photodiode and can be uncovered through Fourier-domain filtering. INDEX TERMS Frequency-domain analysis, multispectral imaging, optical diffraction, photodiode This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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High spatial resolution hyperspectral camera based on exponentially variable filter

Cited by 7 publications

References 8 publications

The Detection of Concealed Targets in Woodland Areas Using Hyperspectral Imagery

The Detection of Concealed Targets in Woodland Areas Using Hyperspectral Imagery

A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing

Single-Point Detection Architecture via Liquid Crystal Modulation for Hyperspectral Imaging Systems

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