&lt;title&gt;Advanced band sharpening study&lt;/title&gt;

Vrabel, James C.

doi:10.1117/12.280586

Cited by 6 publications

(8 citation statements)

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“…The second metric measure is the spectral angle error as expressed in Eq. (15) . It takes the average over all the spatial pixels, and the metric unit is angle degree.…”

Section: Experiments and Test Settingmentioning

confidence: 95%

“…(3) Sharpening Color Normalization (SCN) [15]: The low-res HSI data are first over-sampled to the same pixel size as the high-res pen-chromatic image (HRI). The algorithm multiplies each of the HSI bands by the high-res HRI image and the resulting values are each normalized by the averaged HSI data over the spectral bands covered in the panchromatic range of HRI.…”

Section: Conventional Pan-sharpening Techniquesmentioning

confidence: 99%

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Robust chemical and chemical-resistant material detection using hyper-spectral imager and a new bend interpolation and local scaling HSI sharpening method

2015

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We present new results from our ongoing research activity for chemical threat detection using hyper-spectral imager (HSI) detection techniques by detecting nontraditional threat spectral signatures of agent usage, such as protective equipment, coatings, paints, spills, and stains that are worn by human or on trucks or other objects. We have applied several current state-of-the-art HSI target detection methods such as Matched Filter (MF), Adaptive Coherence Estimator (ACE), Constrained Energy Minimization (CEM), and Spectral Angle Mapper (SAM). We are interested in detecting several chemical related materials: (a) Tyvek clothing is chemical resistance and Tyvek coveralls are one-piece garments for protecting human body from harmful chemicals, and (b) ammonium salts from background could be representative of spills from scrubbers or related to other chemical activities. The HSI dataset that we used for detection covers a chemical test field with more than 50 different kinds of chemicals, protective materials, coatings, and paints. Among them, there are four different kinds of Tyvek material, three types of ammonium salts, and one yellow jugs. The imagery cube data were collected by a HSI sensor with a spectral range of 400-2,500nm. Preliminary testing results are promising, and very high probability of detection (Pd) and low probability of false detection are achieved with the usage of full spectral range (400-2,500nm). In the second part of this paper, we present our newly developed HSI sharpening technique. A new Band Interpolation and Local Scaling (BILS) method has been developed to improve HSI spatial resolution by 4-16 times with a low-cost high-resolution pen-chromatic camera and a RGB camera. Preliminary results indicate that this new technique is promising.

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“…The second metric measure is the spectral angle error as expressed in Eq. (15) . It takes the average over all the spatial pixels, and the metric unit is angle degree.…”

Section: Experiments and Test Settingmentioning

confidence: 95%

Section: Conventional Pan-sharpening Techniquesmentioning

confidence: 99%

Robust chemical and chemical-resistant material detection using hyper-spectral imager and a new bend interpolation and local scaling HSI sharpening method

2015

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“…Sharpening Color Normalization (SCN) [5]. The color-normalization algorithm conventionally used in multispectral imaging is modified for HSI sharpening.…”

Section: Review and Demonstration Of Sharpening Algorithmsmentioning

confidence: 99%

Multisensor Fusion With Hyperspectral Imaging Data: Detection and Classification

Hsu

Burke

2005

Handbook of Pattern Recognition and Computer Vision

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“…The function automatically resamples three MS bands to the high-resolution pixel size using a nearest neighbor, bilinear or cubic convolution technique. The output RGB images will have the pixel size of the input high-resolution data [Vrabel, 1996;Bovolo et al, 2010]. The Brovey transform is defined as the equation [1],…”

Section: Brovey Sharpeningmentioning

confidence: 99%

“…To date, numerous algorithms for image pan-sharpening have been developed in order to combine the spatial information of high resolution PAN image with the spectral information of a lower resolution MS image to produce a high resolution MS image. Some widely performed pan-sharpening algorithms in the remote sensing community are the Intensity-Hue-Saturation (IHS) [Schetsellar, 1998;Choi et al, 2000;Choi, 2006;Myungjin, 2006], the Principal Component Analysis (PCA) [Chavez 20 and Kwarteng, 1989;Shettigara, 1992;Vrabel et al, 1996;Shah, 2008;Yang and Gong, 2012], the Brovey transform [Earth Resource Mapping Pty Ltd., 1990;Chaves, 1991;Du et al, 2007;Bovolo et al, 2010], and the Gram-Schmidt (GS) [Laben et al, 2000]. These pan-sharpening techniques are performed on the pixel level because of the minimum information loss during the sharpening process, so the digital classification accuracy of the pixel level fusion is the highest [Zhang, 1999].…”

Section: Introductionmentioning

confidence: 99%

Spectral and spatial quality analysis of pan-sharpening algorithms: A case study in Istanbul

Sarp

2014

European Journal of Remote Sensing

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In this paper, the performance of four different image pan-sharpening methods, the Brovey, the Gram-Schmidt (GS), the Intensity-Hue-Saturation (IHS) and the Principle Component Analysis (PCA), are investigated based on spectral and spatial distortions. In the study, the Brovey, the GS, the IHS, and the PCA pan-sharpening algorithms are applied to multispectral (MS) bands of Ikonos and QuickBird images. The spectral and spatial qualities of pansharpened images are tested using the Correlation Coefficient (CC), the Root Mean Square Error (RMSE), and the Structural Similarity Index (SSIM). A comparative performance analysis of the CC, the RMSE, and the SSIM shows that the PCA followed by the GS, the Brovey, and the IHS perform the best among all the techniques, except a swap in the PCA and the GS in the SSIM.

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<title>Advanced band sharpening study</title>

Cited by 6 publications

References 0 publications

Robust chemical and chemical-resistant material detection using hyper-spectral imager and a new bend interpolation and local scaling HSI sharpening method

Robust chemical and chemical-resistant material detection using hyper-spectral imager and a new bend interpolation and local scaling HSI sharpening method

Multisensor Fusion With Hyperspectral Imaging Data: Detection and Classification

Spectral and spatial quality analysis of pan-sharpening algorithms: A case study in Istanbul

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