Current status of Hyperspectral Imager Suite (HISUI) onboard International Space Station (ISS)

Matsunaga, Tsuneo; Iwasaki, Akira; Tsuchida, Seigi; Iwao, Koki; Tanii, Jun; Kashimura, Osamu; Nakamura, Ryosuke; Yamamoto, Hirokazu; Kato, Soushi; Obata, Kenta; Mouri, Koichiro; Tachikawa, Tetsushi

doi:10.1109/igarss.2017.8126989

Cited by 35 publications

(27 citation statements)

References 2 publications

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“…The HISUI developed by the Japanese Government will be delivered to the ISS in the near future. 13 Since HISUI has a narrow swath width of 30 km and no pointing function, its coverage is predicted to be patchy. The proposed method is, therefore, expected to be used for compensating HISUI's observation gaps by combining images of MS sensors such as ASTER.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, HS satellite imagers often sacrifice the spatial resolution and/or the swath width of an acquired image for many spectral bands due to some limitations like satellite to ground communication bandwidth, e.g., the spatial resolution and the swath width of Hyperion onboard the EO-1 satellite are 30 m and 7.6 km, respectively, 10 those of the Environmental Mapping and Analysis Program (EnMAP) are 30 m and 30 km, respectively, 11 those of the Precursore Iperspettrale della Missione Applicativa (PRISMA) mission are 30 m and 30 km, respectively, 12 and those of Hyperspectral Imager Suite (HISUI) on the International Space Station (ISS) are 20 m (cross track) or 30 m (along track), and 20 km, respectively. 13 Since this limitation results in reducing the observation frequency, imaging coverage achieved through a mission period is often much smaller in HS satellite sensor projects than in MS satellite sensor projects, leaving gaps in data coverage. Particularly, HISUI onboard the ISS is predicted to leave many gaps after the mission period of three years, because it has no pointing function and its viewing direction is fixed to the nadir.…”

Section: Introductionmentioning

confidence: 99%

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Mineral discrimination by combination of multispectral image and surrounding hyperspectral image

Hirai¹

2019

J. Appl. Rem. Sens.

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A hyperspectral (HS) imager is more effective than a multispectral (MS) imager in mineral discrimination, but spatial coverage of HS images is limited in comparison to that of MS images. Thus Kruse and Perry have proposed a method that uses coincident HS imaging and MS imaging data to extend mineral mapping to larger areas. We propose a method modified from the Kruse and Perry's (K&P) method. Though the K&P method derives the MS-based endmember spectra by weighting the HS-based endmember spectra with the response functions of the MS sensor bands, the proposed method obtains the MS-based endmember spectra from surface reflectance spectra of the MS pixels at the same positions with the HS pixels selected as the HS-based endmembers in the overlapping area. The validation study using airborne visible/infrared imaging spectrometer and advanced spaceborne thermal emission and reflection radiometer images over Cuprite and Goldfield areas, Nevada, USA, demonstrates that the proposed method is more robust against spectral inconsistency between the HS-and the MS-images caused by calibration and/or atmospheric correction errors than the K&P method, though the proposed method is more sensitive to co-registration errors between the HS-and the MS-images than the K&P method. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mineral discrimination by combination of multispectral image and surrounding hyperspectral image

Hirai¹

2019

J. Appl. Rem. Sens.

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“…In recent years, hyperspectral remote sensing has developed rapidly. For example, Italy launched the PRecursore IperSpettrale della Missione Applicativa (PRISMA) earth observation satellite in March 2019 [1], Japan launched the Hyperspectral Imager Suite (HISUI) hyperspectral satellite sensor in 2019 [2], India launched the ISRO's Hyperspectral Imaging Satellite (HYSIS) hyperspectral satellite in 2018 [3], and Germany launched the DLR Earth Sensing Imaging Spectrometer (DESIS) hyperspectral satellite in 2018 and are planning to launch the Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite in 2020 [4,5]. The development of the hyperspectral satellite field will bring about new requirements for image processing.…”

Section: Introductionmentioning

confidence: 99%

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

et al. 2020

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The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted a comprehensive evaluation study of fusing GF-5 hyperspectral data with three typical multispectral data sources (i.e., GF-1, GF-2 and Sentinel-2A (S2A)), based on quantitative metrics, classification accuracy, and computational efficiency. Datasets on three study areas of China were utilized to design numerous experiments, and the performances of nine state-of-the-art fusion methods were compared. Experimental results show that LANARAS (this method was proposed by lanaras et al.), Adaptive Gram–Schmidt (GSA), and modulation transfer function (MTF)-generalized Laplacian pyramid (GLP) methods are more suitable for fusing GF-5 with GF-1 data, MTF-GLP and GSA methods are recommended for fusing GF-5 with GF-2 data, and GSA and smoothing filtered-based intensity modulation (SFIM) can be used to fuse GF-5 with S2A data.

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“…Japan has a future hyperspectral mission, called Hyper-spectral Imager SUIte (HISUI), which will be installed on the Japanese Experiment Module (JEM) of the International Space Station (ISS) in 2019 [4]. HISUI is a pushbroom type hyperspectral imager whose spectral coverage is 0.4-2.5 µm with intervals of 10 nm in visible and near infrared wavelength ranges and 12.5 nm in shortwave infrared wavelength range, respectively [4,5]. Planned spatial resolution of the HISUI is 30 m. Similar to other satellite missions, the HISUI mission plans to provide map-projected image products.…”

Section: Introductionmentioning

confidence: 99%

“…[7] to accurately determine sensor attitude at each observation. HISUI will include an STT for determining its attitude during image acquisition [4]. On the JEM, however, it is expected that attitude determination by STTs will be somewhat limited because half of an STT's field of view will be covered by large solar panels and large modules of the ISS [8], which will reduce the number of stars in an STT's field of view.…”

Section: Introductionmentioning

confidence: 99%

Automated Attitude Determination for Pushbroom Sensors Based on Robust Image Matching

et al. 2018

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Accurate attitude information from a satellite image sensor is essential for accurate map projection and reducing computational cost for post-processing of image registration, which enhance image usability, such as change detection. We propose a robust attitude-determination method for pushbroom sensors onboard spacecraft by matching land features in well registered base-map images and in observed images, which extends the current method that derives satellite attitude using an image taken with 2-D image sensors. Unlike 2-D image sensors, a pushbroom sensor observes the ground by changing its position and attitude according to the trajectory of a satellite. To address pushbroom-sensor observation, the proposed method can trace the temporal variation in the sensor attitude by combining the robust matching technique for a 2-D image sensor and a non-linear least squares approach, which can express gradual time evolution of the sensor attitude. Experimental results using images taken from a visible and near infrared pushbroom sensor of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra as test image and Landsat-8/OLI images as a base map show that the proposed method can determine satellite attitude with an accuracy of 0.003° (corresponding to the 2-pixel scale of ASTER) in roll and pitch angles even for a scene in which there are many cloud patches, whereas the determination accuracy remains 0.05° in the yaw angle that does not affect accuracy of image registration compared with the other two axes. In addition to the achieved attitude accuracy that was better than that using star trackers (0.01°) regarding roll and pitch angles, the proposed method does not require any attitude information from onboard sensors. Therefore, the proposed method may contribute to validating and calibrating attitude sensors in space, at the same time better accuracy will contribute to reducing computational cost in post-processing for image registration.

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Current status of Hyperspectral Imager Suite (HISUI) onboard International Space Station (ISS)

Cited by 35 publications

References 2 publications

Mineral discrimination by combination of multispectral image and surrounding hyperspectral image

Mineral discrimination by combination of multispectral image and surrounding hyperspectral image

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

Automated Attitude Determination for Pushbroom Sensors Based on Robust Image Matching

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