Hyperspectralimagesdataset

Traditionally, hyperspectral imaging has focused solely on the number of spectral bands available for data analysis. Today, however, much more is demanded of hyperspectral imagers, including portability, flexibility, real-time data access and analysis, and video spectroscopy. These features characterize the current drivers of imaging spectroscopy. Our mission at Cubert is to turn these technical properties into a successful user experience and demystify spectral products.

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Hyperspectralimages

Hyperspectral imaging, also known as imaging spectroscopy, acquires simultaneous images in a high number of spectral bands, so that for each pixel of the resulting image, a continuous reflectance spectrum can be derived. The outputs of these measurements are collected in spectral datacubes and serve as inputs for data processing, modeling, or machine learning algorithms.

However, with light field hyperspectral imaging, it is possible to derive the precise position of the critical channels and provide them in a customized system, which will meet the needs of price, spatial, and spectral resolution.

Spectralimaging CT

Since the 1980s, hyperspectral imaging has enabled the development of a wide range of narrowband indices and spectral feature fitting methods used to determine various characteristics. These indices allow the retrieval of specific information, such as vitality status, chlorophyll content, water content, dry matter, or leaf area index—just to name a few of the parameters valuable for agriculture or forestry. Most of these indices are based on studies for specific problems; consequently, they use a wide range of different wavelengths.

However, a hyperspectral camera can perform multiple analyses simultaneously, instead of focusing on one index, like NDVI, as multispectral cameras do. This ability helps our customers perform a variety of applications with the same camera.Today, machine learning algorithms can be trained with either hyperspectral or multispectral datasets. These algorithms are designed to find the specific differences in the spectral signatures of various materials. Since hyperspectral datasets offer much greater spectral detail, machine learning algorithms benefit greatly from these datasets, resulting in a significantly increased specificity of the classifier.

Hyperspectral imaging is now an indispensable tool for a growing number of applications, as well as for research and development. A broad range of applications benefits from the increased specificity of wavelength discrimination of the imagers and the large number of possible applications that just one device can cover. Off-the-shelf hyperspectral imagers offer a very cost-effective solution for every application where only a small number of cameras are needed. But is there a more cost-efficient solution if the application needs dozens or hundreds of cameras?

Spectralinformation in remote sensing

Spectral imagesmeaning

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Spectralimaging camera

The usual answer to this problem is to use off-the-shelf multispectral imaging cameras due to their lower cost. But we are confident that this answer is wrong. Off-the-shelf multispectral imagers either lack the necessary spectral resolution or have available channels at the wrong wavelength. Furthermore, the tolerances of the spectral center position, due to the complicated manufacturing process, are too high for a smooth integration of a large number of cameras into one solution.

Dr. Matthias Locherer has been the Sales Director at Cubert GmbH since 2017. With a PhD in Earth Observation from Ludwig Maximilian University of Munich, he brings extensive expertise in remote sensing, spectral imaging, and data analysis. Matthias has contributed to numerous research projects and publications, particularly in the hyperspectral monitoring of biophysical and biochemical parameters using hyperspectral satellite missions. His deep knowledge of optical measurement techniques and physical modeling makes him a key driver in advancing innovative hyperspectral technologies at Cubert.

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Cubert is the only provider worldwide that delivers hyperspectral video spectrometers that capture full cubes of continuous spectra in less than a millisecond.

Spectralimage ghost

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The cross-correlation matrix given for each classification shows a maximum error of 33% for the multispectral imager, whereas the hyperspectral imager has a maximum error of 4.3%.

Imaging is the method by which an object’s form is reproduced. In spectral imaging, this method uses multiple bands across the electromagnetic spectrum to create an image from the simultaneous measurement of spectra and spatial information. The spectral bands used to create the images can either be discrete or contiguous. The use of discrete bands to create images is called multispectral imaging; the use of contiguous bands is called hyperspectral imaging, or simply spectral imaging, and this produces an image with very high spectral resolution.

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Spectralimage Processing

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Spectrometers were originally one-dimensional point sensors, but recent developments have expanded from point to pixel, making spectral imaging possible and allowing us to examine the spatial distribution of chemical components, materials, or quantity differences.

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Spectroscopy is the study of the interaction of matter with electromagnetic radiation as a function of the frequency of the radiation. Different materials will absorb or reflect this radiation due to their chemical and physical properties, yielding a unique spectral signature. Since each material has a different spectral signature, this data not only has the potential to separate specific materials from others (qualitative spectroscopy), but also allows quantitative statements to be made about the analyzed object.

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