Determination of combined measurement uncertainty via Monte Carlo analysis for the imaging spectrometer ROSIS

Lenhard, Karim

doi:10.1364/ao.51.004065

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…Through the traceable spectral and radiometric calibration of the data, information on the uncertainties involved in the measurement process [17], [18] can now be provided. This is necessary to perform valid measurements [16] and allows potential users to ascertain the usability of the data from these sensors for their use cases.…”

Section: Discussionmentioning

confidence: 99%

Independent Laboratory Characterization of NEO HySpex Imaging Spectrometers VNIR-1600 and SWIR-320m-e

Lenhard

Baumgartner

Schwarzmaier

2015

IEEE Trans. Geosci. Remote Sensing

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The Remote Sensing Technology Institute (Institut für Methodik der Fernerkundung) of the German Aerospace Agency (DLR) operates two sensors for airborne hyperspectral imaging, i.e., a Norsk Elektro Optikk A/S (NEO) HySpex VNIR-1600 and a NEO HySpex SWIR-320m-e. Since these sensors are used for the development of physically based inversion algorithms, atmospheric correction algorithms and for calibration/ validation activities, their properties need to be characterized in detail, and an accurate calibration is mandatory. The characterization is performed at the calibration laboratory of DLR for imaging spectrometers in Oberpfaffenhofen. Key results of the characterization are assessments of the radiometric, spectral, and geometric performances, including the typical optical distortions prevalent in pushbroom imaging spectrometers, keystone and smile, and the associated measurement uncertainties. Potential sources of systematic error, the detector nonlinearity and the polarization sensitivity are discussed. The radiometric calibration is traceably performed to the German national metrology institute Physikalisch-Technische Bundesanstalt, whereas the spectral measurements can be traced back to the spectral properties of atomic line lamps. The implemented level 0 to level 1 calibration procedure is presented as well.

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Section: Discussionmentioning

confidence: 99%

Independent Laboratory Characterization of NEO HySpex Imaging Spectrometers VNIR-1600 and SWIR-320m-e

Lenhard

Baumgartner

Schwarzmaier

2015

IEEE Trans. Geosci. Remote Sensing

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“…The proposed scheme was evaluated over two remote sensing images acquired by two different sensors, ROSIS-03 and AVIRIS. The main specifications of these sensors are detailed in Table 1 [37][38][39][40][41][42]. This image was acquired by the ROSIS-03 hyperspectral sensor covering the spectrum between 430 and 860 nm in 115 spectral bands over the city of Pavia.…”

Section: Datamentioning

confidence: 99%

TCANet for Domain Adaptation of Hyperspectral Images

2019

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The use of Convolutional Neural Networks (CNNs) to solve Domain Adaptation (DA) image classification problems in the context of remote sensing has proven to provide good results but at high computational cost. To avoid this problem, a deep learning network for DA in remote sensing hyperspectral images called TCANet is proposed. As a standard CNN, TCANet consists of several stages built based on convolutional filters that operate on patches of the hyperspectral image. Unlike the former, the coefficients of the filter are obtained through Transfer Component Analysis (TCA). This approach has two advantages: firstly, TCANet does not require training based on backpropagation, since TCA is itself a learning method that obtains the filter coefficients directly from the input data. Second, DA is performed on the fly since TCA, in addition to performing dimensional reduction, obtains components that minimize the difference in distributions of data in the different domains corresponding to the source and target images. To build an operating scheme, TCANet includes an initial stage that exploits the spatial information by providing patches around each sample as input data to the network. An output stage performing feature extraction that introduces sufficient invariance and robustness in the final features is also included. Since TCA is sensitive to normalization, to reduce the difference between source and target domains, a previous unsupervised domain shift minimization algorithm consisting of applying conditional correlation alignment (CCA) is conditionally applied. The results of a classification scheme based on CCA and TCANet show that the DA technique proposed outperforms other more complex DA techniques.

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“…Lenhard (2012). However, when considering the processing chain as a whole and when investigating the generic case, it does not seem to be feasible anymore (Beekhuizen et al, 2009a,b).…”

Section: Recommendations From Metrologymentioning

confidence: 99%