Hyperspectral Remote Sensing

Borengasser, Marcus; Hungate, William S.; Watkins, Russell

doi:10.1201/9781420012606

Cited by 170 publications

(35 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hyperspectral (HS) imaging has gained increasing interest in many fields and applications. These techniques can be found in airborne and remote sensing applications [ 1 , 2 , 3 , 4 ], biomedical and medical studies [ 5 , 6 , 7 ], food and agricultural monitoring [ 8 , 9 , 10 ], forensic applications [ 11 , 12 ], and many more. The HS images captured for these applications are usually arranged in three-dimensional (3D) datacubes, which include two dimensions (2D) for the spatial information and one additional dimension (1D) for the spectral information.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Oiknine¹,

August²,

Farber³

et al. 2018

J. Imaging

View full text Add to dashboard Cite

Hyperspectral (HS) imaging involves the sensing of a scene’s spectral properties, which are often redundant in nature. The redundancy of the information motivates our quest to implement Compressive Sensing (CS) theory for HS imaging. This article provides a review of the Compressive Sensing Miniature Ultra-Spectral Imaging (CS-MUSI) camera, its evolution, and its different applications. The CS-MUSI camera was designed within the CS framework and uses a liquid crystal (LC) phase retarder in order to modulate the spectral domain. The outstanding advantage of the CS-MUSI camera is that the entire HS image is captured from an order of magnitude fewer measurements of the sensor array, compared to conventional HS imaging methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Oiknine¹,

August²,

Farber³

et al. 2018

J. Imaging

View full text Add to dashboard Cite

show abstract

“…Hyperspectral sensors overcome this limitation by producing images in which each pixel contains the spectral signature of a scene in hundreds of narrow spectral bands [ 3 ]. Panchromatic and RGB images are restricted to one and three channels, respectively, however hyperspectral images are captured in many channels.…”

Section: Introductionmentioning

confidence: 99%

Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance

Hobbs

Lambert

Ryan³

et al. 2021

Sensors

View full text Add to dashboard Cite

Near infrared (NIR) remote sensing has applications in vegetation analysis as well as geological investigations. For extra-terrestrial applications, this is particularly relevant to Moon, Mars and asteroid exploration, where minerals exhibiting spectral phenomenology between 600 and 800 nm have been identified. Recent progress in the availability of processors and sensors has created the possibility of development of low-cost instruments able to return useful scientific results. In this work, two Raspberry Pi camera types and a panchromatic astronomy camera were trialed within a pushbroom sensor to determine their utility in measuring and processing the spectrum in reflectance. Algorithmic classification of all 15 test materials exhibiting spectral phenomenology between 600 and 800 nm was easily performed. Calibration against a spectrometer considers the effects of the sensor, inherent image processing pipeline and compression. It was found that even the color Raspberry Pi cameras that are popular with STEM applications were able to record and distinguish between most minerals and, contrary to expectations, exploited the infra-red secondary transmissions in the Bayer filter to gain a wider spectral range. Such a camera without a Bayer filter can markedly improve spectral sensitivity but may not be necessary.

show abstract

“…Airborne or spaceborne hyperspectral imaging sensors provide remote sensing image cubes from the reflected solar radiation from the Earth's surface (Bingwen et al, 2013;Borengasser et al, 2007;Zhang & Du, 2012). A hyperspectral image cube contains very narrow bands covering from the visible spectrum through the infrared spectrum (Bakhshi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Improving hyperspectral sub-pixel target detection in multiple target signatures using a revised replacement signal model

Khoshboresh-Masouleh

Hasanlou

2020

European Journal of Remote Sensing

View full text Add to dashboard Cite

Hyperspectral Remote Sensing

Cited by 170 publications

References 0 publications

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance

Improving hyperspectral sub-pixel target detection in multiple target signatures using a revised replacement signal model

Contact Info

Product

Resources

About