MightySat II.1 hyperspectral imager: summary of on-orbit performance

Yarbrough, S. A.; Caudill, Thomas R.; Kouba, Eric; Osweiler, Victor; Arnold, James E.

doi:10.1117/12.453339

Cited by 17 publications

(7 citation statements)

References 3 publications

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“…Once sampled and corrected for nonuniformities in response to the detector array elements, data processing is the same as the Michelson FTS [ Griffiths and de Haseth , 1986]. Since Okamoto et al [1984], several different variants of spatial interferometer spectrometers have been produced, principally for remote sensing [e.g., Smith and Schempp , 1991; Rafert et al , 1994; Smith and Hammer , 1996; Minnett and Sellar , 2005; Lucey et al , 2007, 2008], including one that flew in space and collected data in the visible portion of the spectrum [ Yarbrough et al , 2002]. IIM is the first planetary application of this technology.…”

Section: Introductionmentioning

confidence: 99%

Global estimates of lunar iron and titanium contents from the Chang' E‐1 IIM data

Xue

Zhao

et al. 2012

J. Geophys. Res.

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[1] Until recently, global high spatial resolution maps of FeO and TiO 2 of the Moon were only derived from Clementine data. In this study, we show global maps of FeO and TiO 2 using Chang'E-1 Interference Imaging Spectrometer (IIM) at a spatial resolution of 200 m/pixel. With a newly developed calibration presented here, spectra obtained by IIM compare well with telescopic spectra. Spectral parameters previously shown to be sensitive to iron and titanium, derived from the calibrated IIM data are highly correlated with the measured elemental concentration with R 2 = 0.96 for FeO and 0.95 for TiO 2 . The maps were developed using this calibration. Histograms of basalt FeO estimates have a negatively skewed distribution, while TiO 2 distributions are unimodal. They also revealed that the lunar highland crust is relatively uniform on the quadrant scale (several hundred to thousand kilometers scale) but inhomogenous on the global scale. The area of highest elevation of the Moon has very low FeO and TiO 2 raising the question about South Pole-Aitken (SPA) (whether its ejecta deposits covered the highest elevation and when it was formed). Although the average FeO and TiO 2 abundances for basalts are highly correlated, local areas of elevated iron can be associated with both high and low titanium.

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Section: Introductionmentioning

confidence: 99%

Global estimates of lunar iron and titanium contents from the Chang' E‐1 IIM data

Xue

Zhao

et al. 2012

J. Geophys. Res.

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“…表 1 不同横向剪切干涉仪的干涉图调制度表达式 [27] , 在轨运行 2 年. Xiangli 等 [28] 研制的我国 "环境 -1A" 卫星高光谱成像仪于 2008 年发射, 至今仍在轨稳定运行.…”

Section: Fixed Mirrorunclassified

Fourier transform imaging spectroscopy

Xiangli¹,

Lv²,

Cai³

et al. 2020

Sci. Sin.-Inf.

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“…Focusing on spaceborne sensors, we can highlight the Earth Observing-1 (EO-1) Hyperion [11,12], which also records information in the range 0.4-2.5 µm with 10nm spectral resolution, obtaining data cubes with 220 spectral bands. Other spectrometers allocated on satellite-platforms are the Moderate Resolution Imaging Spectroradiometer (MODIS) [13], the Fourier Transform HyperSpectral Imager (FTHSI) [14], and the CHRIS Proba [15]. New missions include the German Environmental Mapping and Analysis Program (EnMAP) [16], the Italian Precursore IperSpettrale della Missione Applicativa (PRISMA) program [17], the commercial Space-borne Hyperspectral Applicative Land and Ocean Mission (SHALOM) [18], the NASA Hyperspectral Infrared Imager (HyspIRI) [19], or the Japanese Hyperspectral Imager Suite (HISUI) [20].…”

Section: Hyperspectral Imaging Concept and Missionsmentioning

confidence: 99%

Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

et al. 2018

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Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.

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MightySat II.1 hyperspectral imager: summary of on-orbit performance

Cited by 17 publications

References 3 publications

Global estimates of lunar iron and titanium contents from the Chang' E‐1 IIM data

Global estimates of lunar iron and titanium contents from the Chang' E‐1 IIM data

Fourier transform imaging spectroscopy

Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

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