EO-1 Hyperion hyperspectral aggregation and comparison with EO-1 Advanced Land Imager and Landsat 7 ETM+

Barry, P.S.; Mendenhall, Jeffrey A.; Jarecke, Peter J.; Folkman, Mark A.; Pearlman, Jay; Markham, B.L.

doi:10.1109/igarss.2002.1026209

Cited by 18 publications

(25 citation statements)

References 3 publications

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“…The use of simulated HyspIRI bands for any hyperspectral models provides valuable information for next generation hyperspectral sensors from which users will have to extract appropriate optimal wavebands relevant for their application, or, as an alternative, they could carry specialized optimal sensors with selective wavebands, focusing on gathering data for targeted applications [34]. Therefore, we simulated Hyperion, CHRIS and HyspIRI spectral bands by using a Gaussian spectral response function on the field R rs data [34][35][36]. For the Hyperion spectral bandwidth simulation, we used the bands centered at: 599.8, 609.97, 620.15, 650.67, 701.55, 711.72 and 721.9 nm.…”

Section: Sensor Analysismentioning

confidence: 99%

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

Ogashawara

Mishra

Mishra³

et al. 2013

Remote Sensing

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Abstract:We evaluated the accuracy and sensitivity of six previously published reflectance based algorithms to retrieve Phycocyanin (PC) concentration in inland waters. We used field radiometric and pigment data obtained from two study sites located in the United States and Brazil. All the algorithms targeted the PC absorption feature observed in the water reflectance spectra between 600 and 625 nm. We evaluated the influence of chlorophyll-a (chl-a) absorption on the performance of these algorithms in two contrasting environments with very low and very high cyanobacteria content. All algorithms performed well in low to moderate PC concentrations and showed signs of saturation or decreased sensitivity for high PC concentration with a nonlinear trend. MM09 was found to be the most accurate algorithm overall with a RMSE of 15.675%. We also evaluated the use of these algorithms with the simulated spectral bands of two hyperspectral space borne sensors including Hyperion and Compact High-Resolution Imaging Spectrometer (CHRIS) and a hyperspectral air borne sensor, Hyperspectral Infrared Imager (HyspIRI). Results showed that the sensitivity for chl-a of PC retrieval algorithms for Hyperion simulated data were less noticable than using the spectral bands of CHRIS; HyspIRI results show that SC00 could be used for this sensor with low chl-a influence. This review of reflectance OPEN ACCESS Remote Sens. 2013, 5 4775 based algorithms can be used to select the optimal approach in studies involving cyanobacteria monitoring through optical remote sensing techniques.

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Section: Sensor Analysismentioning

confidence: 99%

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

Ogashawara

Mishra

Mishra³

et al. 2013

Remote Sensing

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“…To compare the details of the regions of Datasets II and III, we selected a small area enclosed by rectangles in Figs [14][15][16]. Interpretation of each group of images in Figs.…”

Section: A Comparison Between Spectra-enhanced and Real Data By Visumentioning

confidence: 99%

“…However, a massive amount of accumulated MS data has been collected around the world, and these MS data usually have higher spatial and time resolution and a wider swath than HS data. For example, MS images captured by the Advanced Land Imager (ALI) (also carried on EO-1) have the same spatial resolution as Hyperion products, but have a swath of 37 km, and the images captured by the Landsat Thematic Mapper 5/Enhanced Thematic Mapper (TM/ETM+) have a swath of 185 km [15], [16]. As the Landsat satellites and EO-1 satellite operate on the same track, the HS or MS sensors onboard can obtain data from the same location with a very small time difference.…”

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confidence: 99%

Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image

Sun

Zhang

Yang

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral (HS) remote sensing has an important role in a wide variety of fields. However, its rapid progress has been constrained due to the narrow swath of HS images. This paper proposes a spectral resolution enhancement method (SREM) for remotely sensed multispectral (MS) image, to generate wide swath HS images using auxiliary multi/hyper-spectral data. Firstly, a set number of spectra of different materials are extracted from both the MS and HS data. Secondly, the approach makes use of the linear relationships between multi and hyper-spectra of specific materials to generate a set of transformation matrices. Then, a spectral angle weighted minimum distance (SAWMD) matching method is used to select a suitable matrix to create HS vectors from the original MS image, pixel by pixel. The final result image data has the same spectral resolution as the original HS data that used and the spatial resolution and swath were also the same as for the original MS data. The derived transformation matrices can also be used to generate multitemporal HS data from MS data for different periods. The approach was tested with three image datasets, and the spectra-enhanced and real HS data were compared by visual interpretation, statistical analysis, and classification to evaluate the performance. The experimental results demonstrated that SREM produces good image data, which will not only greatly improve the range of applications for HS data but also encourage more utilization of MS data.Index Terms-Date fusion, hyperspectral (HS) image, multispectral (MS) image, multitemporal, spectral resolution enhancement.

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“…Hyperspectral data has been considered in remote sensing to crosscalibrate a hyperspectral sensor with another hyperspectral or multispectral sensor (Teillet et al, 2001), and to simulate data of future sensors (Barry et al, 2002). The algorithm exploits sensor simulation capabilities of hyperspectral data using spectral response function of the sensor to be simulated.…”

Section: Generating Simulated Multispectral (Sms) Bandsmentioning

confidence: 99%

“…Some methods directly convolve the multispectral filter functions to the hyperspectral data (Green and Shimada, 1997), which is equivalent to using the values of the multispectral SRF as the weighting factors. Some have used the integral of the product of the hyperspectral and multispectral SRFs as the weight (Barry et al, 2002). Few have calculated the weights by finding the least square approximation of a multispectral SRF by a linear combination of the hyperspectral SRFs ( Slawomir Blonksi, Gerald Blonksi, Jeffrey Blonksi, Robert Ryan, Greg Terrie, Vicki Zanoni, 2001).…”

Section: Generating Simulated Multispectral (Sms) Bandsmentioning

confidence: 99%

Sensor Simulation based Hyperspectral Image Enhancement with Minimal Spectral Distortion

Khandelwal

Rajan

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In the recent past, remotely sensed data with high spectral resolution has been made available and has been explored for various agricultural and geological applications. While these spectral signatures of the objects of interest provide important clues, the relatively poor spatial resolution of these hyperspectral images limits their utility and performance. In this context, hyperspectral image enhancement using multispectral data has been actively pursued to improve spatial resolution of such imageries and thus enhancing its use for classification and composition analysis in various applications. But, this also poses a challenge in terms of managing the trade-off between improved spatial detail and the distortion of spectral signatures in these fused outcomes. This paper proposes a strategy of using vector decomposition, as a model to transfer the spatial detail from relatively higher resolution data, in association with sensor simulation to generate a fused hyperspectral image while preserving the inter band spectral variability. The results of this approach demonstrates that the spectral separation between classes has been better captured and thus helped improve classification accuracies over mixed pixels of the original low resolution hyperspectral data. In addition, the quantitative analysis using a rank-correlation metric shows the appropriateness of the proposed method over the other known approaches with regard to preserving the spectral signatures.

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EO-1 Hyperion hyperspectral aggregation and comparison with EO-1 Advanced Land Imager and Landsat 7 ETM+

Cited by 18 publications

References 3 publications

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image

Sensor Simulation based Hyperspectral Image Enhancement with Minimal Spectral Distortion

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