Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

Kumar, Ankush; Keerthi, V.; Manjunath, A. S.; Werff, H.M.A. van der; Meer, Freek D. van der

doi:10.1016/j.jag.2010.03.004

Cited by 22 publications

(5 citation statements)

References 16 publications

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“…To arrive at a mineral map, hard classifiers or mixture modeling are the most popular approaches in geologic remote sensing (Kumar et al, 2010). The spectral angle mapper (Kruse et al, 1993), which calculates the angle between two vectors resulting from endmember spectra and pixel spectra, is often used (Bishop et al, 2011;Tangestani et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Wavelength feature mapping as a proxy to mineral chemistry for investigating geologic systems: An example from the Rodalquilar epithermal system

Meer

Kopačková

Koucká

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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Section: Introductionmentioning

confidence: 99%

Wavelength feature mapping as a proxy to mineral chemistry for investigating geologic systems: An example from the Rodalquilar epithermal system

Meer

Kopačková

Koucká

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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“…Equation ( 6) represents the normalized SDS. The second method is the spectral correlation similarity (SCS), which uses the spectral correlation coefficient between two spectra as a measure of similarity [33]. As it is valid only for a positive correlation, the correlation coefficient is between 0 and 1.…”

Section: Hyperspectral Matching Algorithmmentioning

confidence: 99%

Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

Park

Foucher

et al. 2021

Remote Sensing

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With an increase in the overseas maritime transport of hazardous and noxious substances (HNSs), HNS-related spill accidents are on the rise. Thus, there is a need to completely understand the physical and chemical properties of HNSs. This can be achieved through establishing a library of spectral characteristics with respect to wavelengths from visible and near-infrared (VNIR) bands to shortwave infrared (SWIR) wavelengths. In this study, a ground HNS measurement experiment was conducted for artificially spilled HNS by using two hyperspectral cameras at VNIR and SWIR wavelengths. Representative HNSs such as styrene and toluene were spilled into an outdoor pool and their spectral characteristics were obtained. The relative ratio of HNS to seawater decreased and increased at 550 nm and showed different constant ratios at the SWIR wavelength. Noise removal and dimensional compression procedures were conducted by applying principal component analysis on HNS hyperspectral images. Pure HNS and seawater endmember spectra were extracted using four spectral mixture techniques—N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA). The accuracy of detection values of styrene and toluene through the comparison of the abundance fraction were 99.42% and 99.56%, respectively. The results of this study are useful for spectrum-based HNS detection in marine HNS accidents.

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“…In the SDS method, the similarity is measured by estimating the spectral distance between a target spectrum and a reference spectrum [58]. In the SCS method, the spectral correlation coefficients, ranging from 0 to 1, are used between the target spectrum and the reference spectrum as a measure of similarity [59]. In the SSV method, both the spectral distance between the target spectrum and the reference spectrum and the correlation coefficient are used as a measure of similarity [60], as shown in (4).…”

Section: Spectral Similarity Derivationmentioning

confidence: 99%

Multi-Spectral Ship Detection Using Optical, Hyperspectral, and Microwave SAR Remote Sensing Data in Coastal Regions

Park

Jang

et al. 2018

Sustainability

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The necessity of efficient monitoring of ships in coastal regions has been increasing over time. Multi-satellite observations make it possible to effectively monitor vessels. This study presents the results of ship detection methodology, applied to optical, hyperspectral, and microwave satellite images in the seas around the Korean Peninsula. Spectral matching algorithms are used to detect ships using hyperspectral images with hundreds of spectral channels and investigate the similarity between the spectra and in-situ measurements. In the case of SAR (Synthetic Aperture Radar) images, the Constant False Alarm Rate (CFAR) algorithm is used to discriminate the vessels from the backscattering coefficients of Sentinel-1B SAR and ALOS-2 PALSAR2 images. Validation results exhibited that the locations of the satellite-detected vessels showed good agreement with real-time location data within the Sentinel-1B coverage in the Korean coastal region. This study presented the probability of detection values of optical and SAR-based ship detection and discussed potential causes of the errors. This study also suggested a possibility for real-time operational use of vessel detection from multi-satellite images based on optical, hyperspectral, and SAR remote sensing, particularly in the inaccessible coastal regions off North Korea, for comprehensive coastal management and sustainability.

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Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

Cited by 22 publications

References 16 publications

Wavelength feature mapping as a proxy to mineral chemistry for investigating geologic systems: An example from the Rodalquilar epithermal system

Wavelength feature mapping as a proxy to mineral chemistry for investigating geologic systems: An example from the Rodalquilar epithermal system

Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

Multi-Spectral Ship Detection Using Optical, Hyperspectral, and Microwave SAR Remote Sensing Data in Coastal Regions

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