DESIS (DLR Earth Sensing Imaging Spectrometer for the ISS-MUSES platform)

Eckardt, Andreas; Horack, John M.; Lehmann, Frank; Krutz, David; Drescher, Jurgen; Whorton, Mark; Soutullo, Mike

doi:10.1109/igarss.2015.7326053

Cited by 30 publications

(16 citation statements)

References 3 publications

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“…In recent years, unmanned aerial vehicle (UAV) based real-time hyperspectral imaging has been becoming more common in various applications such as agricultural monitoring, while raising new challenges in image processing. Table I presents the principal parameters of seven spaceborne imaging spectroscopy missions planned for the near future: CCRSS, DESIS [5], EnMAP [6], HISUI [7], PRISMA [8], Shalom [9], and HyspIRI [10]. Many of those satellites are designed to have a ground sampling distance of 30 m, aiming at global coverage.…”

Section: B Missions and Statisticsmentioning

confidence: 99%

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Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

626

266

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Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information, which make a plethora of applications for the analysis of large areas of the Earth surface feasible. However, a huge number of factors, such as high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions, make such data inherently nonlinear and complex, which poses extreme challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide. This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality reduction, resolution enhancement, hyperspectral image denoising and restoration, change detection, and fast computing. For each topic, we provide a synopsis of the state-of-the-art approaches and numerical results for validation and evaluation of different methodologies, followed by a discussion of future challenges and research directions.

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Section: B Missions and Statisticsmentioning

confidence: 99%

“…GPUs are now fully programmable using high-level languages such as NVIDIA CUDA. 5 The GPU specializes in computer-intensive, massive data parallel computation (which describes exactly graphics rendering). Therefore, more transistors can be devoted to data processing rather than data caching and flow control.…”

Section: B Hardware Acceleratorsmentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

626

266

View full text Add to dashboard Cite

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“…Although much research has been done on determining best bands for studying crops, this study contributes unique knowledge due to its use of 99 Hyperion images throughout different AEZs in the US to study five globally dominant crops. The results from this research will help us prepare for processing large datasets that will be generated by upcoming hyperspectral satellites like EnMAP and the Surface Biology and Geology mission (formerly HyspIRI mission) [57], as well as the DLR Earth Sensing Imaging Spectrometer (DESIS) which is already on the ISS-MUSES platform [58], and automating that processing in a cloud-computing platform. This will enable us to study and characterize agricultural crops and advance their modeling and mapping, which in turn will help in advancing food security analysis.…”

Section: Classify the Five Leading Principal Crops In The Us Using Mumentioning

confidence: 99%

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Aneece

Thenkabail

2018

Remote Sensing

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As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

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“…Clearly, further research will be required to validate the image fusion method with multi-sensor real data. The Multi-User System for Earth Sensing (MUSES) platform on the international space station is expected to provide a pair of hyperspectral and multispectral images acquired over the same area under the same environmental conditions, which satisfies the assumption of image fusion [51]. …”

Section: Discussionmentioning

confidence: 99%

Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images

2016

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Spaceborne hyperspectral images are useful for large scale mineral mapping. Acquired at a ground sampling distance (GSD) of 30 m, the Environmental Mapping and Analysis Program (EnMAP) will be capable of putting many issues related to environment monitoring and resource exploration in perspective with measurements in the spectral range between 420 and 2450 nm. However, a higher spatial resolution is preferable for many applications. This paper investigates the potential of fusion-based resolution enhancement of hyperspectral data for mineral mapping. A pair of EnMAP and Sentinel-2 images is generated from a HyMap scene over a mining area. The simulation is based on well-established sensor end-to-end simulation tools. The EnMAP image is fused with Sentinel-2 10-m-GSD bands using a matrix factorization method to obtain resolution-enhanced EnMAP data at a 10 m GSD. Quality assessments of the enhanced data are conducted using quantitative measures and continuum removal and both show that high spectral and spatial fidelity are maintained. Finally, the results of spectral unmixing are compared with those expected from high-resolution hyperspectral data at a 10 m GSD. The comparison demonstrates high resemblance and shows the great potential of the resolution enhancement method for EnMAP type data in mineral mapping.

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DESIS (DLR Earth Sensing Imaging Spectrometer for the ISS-MUSES platform)

Cited by 30 publications

References 3 publications

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images

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