ROSIS imaging spectrometer and its potential for ocean parameter measurements (airborne and space-borne)

Kunkel, B.; Blechinger, Fritz; Viehmann, D.; Piepen, Heinz van der; Doerffer, R.

doi:10.1080/01431169108929691

Cited by 28 publications

(6 citation statements)

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“…Experiments are conducted on seven benchmark datasets. 18,19,49 The main characteristics of each dataset (image dimensions, number of bands, and number of classes) are summarized in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…Several hyperspectral sensors have been used to acquire data for a number of different applications, such as thematic mapping, geology, hydrology, agricultural assessment, mineral exploration, and study of global environmental and climate changes. Some important sensors include the hyperspectral digital imagery collection experiment, 17 airborne visible/infrared imaging spectrometer, 18 reflective optics system imaging spectrometer, 19 compact airborne spectrographic imager, 20 airborne imaging spectrometer, 21 airborne imaging spectrometer for different applications, 22 geophysical and environmental research imaging spectrometer, 23 hyperspectral mapper, 24 aerosol mass spectrometer, 25 airborne prism experiment, 26 compact high-resolution imaging spectrometer, 27 digital airborne imaging spectrometer, 28 and thermal infrared multispectral scanner. 29 The analysis of hyperspectral data has received growing interest from the community, since they have spectral attributes with high discriminative power.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral data classification improved by minimum spanning forests

Silva

Pedrini

2016

J. Appl. Remote Sens

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Remote sensing technology has applications in various knowledge domains, such as agriculture, meteorology, land use, environmental monitoring, military surveillance, and mineral exploration. The increasing advances in image acquisition techniques have allowed the generation of large volumes of data at high spectral resolution with several spectral bands representing images collected simultaneously. We propose and evaluate a supervised classification method composed of three stages. Initially, hyperspectral values and entropy information are employed by support vector machines to produce an initial classification. Then, the K-nearest neighbor technique searches for pixels with high probability of being correctly classified. Finally, minimum spanning forests are applied to these pixels to reclassify the image taking spatial restrictions into consideration. Experiments on several hyperspectral images are conducted to show the effectiveness of the proposed method.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral data classification improved by minimum spanning forests

Silva

Pedrini

2016

J. Appl. Remote Sens

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“…where the first term directly results from L h (ν h ) and L m (ν m ) in (22), whereas the second and third terms from L u (u) and (28). The optimization of L f ( f ) can be performed by forcing its derivative to 0, which yields the update term of f in line 5 of Algorithm 1.…”

Section: Algorithmmentioning

confidence: 99%

“…The matrices h and m were generated as described in Section III. The reference image considered in this work is mainly the classical ROSIS image acquired over Pavia, northern Italy [28] reduced to 256 × 256 pixels and 92 bands. The HS datacube was obtained by applying a 5 × 5 Gaussian lowpass filter in each band and by using a 4:1 decimation ratio ( p = 2).…”

Section: A Simulation Scenariomentioning

confidence: 99%

Spectral Image Fusion From Compressive Measurements

Vargas

Espitia

Argüello

et al. 2019

IEEE Trans. on Image Process.

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Compressive spectral imagers reduce the number of sampled pixels by coding and combining the spectral information. However, sampling compressed information with simultaneous high spatial and high spectral resolution demands expensive high-resolution sensors. This paper introduces a model allowing data from high spatial/low spectral and low spatial/high spectral resolution compressive sensors to be fused. Based on titis model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity terin and smoothness and sparsity regularization penalties. The parameters of the different sensors are optimized and the choice of an appropriate regularization is studied in order to improve the quality of the high resolution reconstructed images. Simulation results conducted on synthetic and real data, with different compressive sampling imagers, allow the quality of the proposed fusion method to be appreciated.

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“…Given the very demanding requirements of APEX together with the experience of calibrating similar instruments in the past, in particular DAIS (Oertel and Zhukov, 1993;Müller and Oertel, 1995;Strobl et al, 1996Strobl et al, , 1997Strobl et al, , 1998Müller et al, 1998) and ROSIS (Kunkel et al, 1991;Gege and Mooshuber, 1997;Gege et al, 1998) it was decided to aim for a facility which unifies state of the art calibration technology and standards with the flexibility to adapt to changing instrument interfaces (Suhr et al, 2005(Suhr et al, , 2006. The Calibration Home Base (CHB), as it now exists ( Fig.…”

Section: Introductionmentioning

confidence: 98%