Normalized Spectral Similarity Score (${\hbox{NS}}^{3}$) as an Efficient Spectral Library Searching Method for Hyperspectral Image Classification

Nidamanuri, ‪Rama Rao; Zbell, Bernd

doi:10.1109/jstars.2010.2086435

Cited by 48 publications

(31 citation statements)

References 25 publications

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“…Assessment of chromatic similarity between animal and background spectra using a nonbiological measure (Spectral Angle Mapper) In the field of remote sensing, automated spectral library search algorithms developed for hyper-spectral images (Chang 2003;Sweet 2003;Freek 2006;Nidamanuri and Zbell 2011) are used to compare reflectance spectra of known targets to those of novel spectra by computing a scalar similarity score between them. For these algorithms, stochastic methods are more frequently used than deterministic methods because imaging conditions can be imperfect and because the high spectral resolution of a hyper-spectral sensor often results in more than one material spectral signature in a given pixel.…”

Section: Study Site Animal and Substrate Measurementsmentioning

confidence: 99%

Quantification of cuttlefish (Sepia officinalis) camouflage: a study of color and luminance using in situ spectrometry

et al. 2012

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Cephalopods are renowned for their ability to adaptively camouflage on diverse backgrounds.Sepia officinalis camouflage body patterns have been characterized spectrally in the laboratory but not in the field due to the challenges of dynamic natural light fields and the difficulty of using spectrophotometric instruments underwater. To assess cuttlefish color match in their natural habitats, we studied the spectral properties of S. officinalis and their backgrounds on the Aegean coast of Turkey using point-by-point in situ spectrometry. Fifteen spectrometry datasets were collected from seven cuttlefish; radiance spectra from animal body components and surrounding substrates were measured at depths shallower than 5m. We quantified luminance and color contrast of cuttlefish components and background substrates in the eyes of hypothetical di-and trichromatic fish predators. Additionally, we converted radiance spectra to sRGB color space to simulate their in situ appearance to a human observer. Within the range of natural colors at our study site, cuttlefish closely matched the substrate spectra in a variety of body patterns.Theoretical calculations showed that this effect might be more pronounced at greater depths. We also showed that a non-biological method ("Spectral Angle Mapper"), commonly used for spectral shape similarity assessment in the field of remote sensing, shows moderate correlation to biological measures of color contrast. This performance is comparable to that of a traditional measure of spectral shape similarity, hue and chroma. This study is among the first to quantify color matching of camouflaged cuttlefish in the wild.3

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Section: Study Site Animal and Substrate Measurementsmentioning

confidence: 99%

Quantification of cuttlefish (Sepia officinalis) camouflage: a study of color and luminance using in situ spectrometry

et al. 2012

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“…Statistical information on spectral bands may not be utilized Granahan and Sweet (2001) (Continued ) 8224 S. Shanmugam and P. SrinivasaPerumal Extensive spectral library with standard data grouping required for accuracy Nidamanuri and Zbell (2011a) JM-SAM-based mixed measure Combines the stochastic JeffriesMatusita distance and deterministic SAM Higher discriminability than JM and SAM Suitability to discriminate and assess proportions within a mixed pixel is yet to be assessed Padma and Sanjeevi (2014) International Journal of Remote Sensing 8225 cluster analysis to the advanced technique of automated matching by adapting to the following factors.…”

Section: Encoding Measuresmentioning

confidence: 99%

“…It is pertinent to mention here that integration of spectral libraries has resulted in increased accuracy of target matching and identification in several applications. Spectral library transfer in spatial (library spectra collected from many geographical locations) and temporal (library spectra collected at different instances) domains has been attempted for assessing spectral variability among crops such as alfalfa, triticale, winter barley, winter rape, winter rye, and winter wheat (Nidamanuri and Zbell 2011a). Spectral libraries in different applications are referred to as either 'spectral signature database' (Ruby and Fischer 2002), 'information service' (Leenaars 2013) or 'look-up table' (Mobley et al 2005).…”

Section: Spectral Librarymentioning

confidence: 99%

“…In this context, it is also pertinent to note that the library architecture plays a major role in the accuracy of the matching process. Commenting on the limitation of sequential library architecture while matching the closely similar crop spectra, Nidamanuri and Zbell (2011a) state that the numerical significance given to each candidate spectrum in the library through sequential architecture will avoid the prospect of identifying subtle differences among the crop spectra.…”

Section: Technical Requirements For Construction Of Librariesmentioning

confidence: 99%

“…SAM also proved to be a good contributor when used in combination with other approaches. When combined with spectral information divergence measures such as SAM-SID (Du et al 2004), with amplitude difference measure as Normalized Spectral Similarity Score (NS 3 ) (Nidamanuri and Zbell 2011a) and Jeffries-Matusita distance as JM-SAM (Padma and Sanjeevi 2014), the SAM approach yielded better accuracy in matching targets. A detailed listing of several matching approaches and a discussion on their advantages and limitations is presented in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Spectral matching approaches in hyperspectral image processing

Sanjeevi

SrinivasaPerumal

2014

International Journal of Remote Sensing

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Many spectral matching algorithms, ranging from the traditional clustering techniques to the recent automated matching models, have evolved. This paper provides a review and up-to-date information on the past and current role of the spectral matching approaches adopted in hyperspectral satellite image processing. The need for spectral matching has been deliberated and a list of spectral matching algorithms has been compared and described. A review of the conventional spectral angle measures and the advanced automated spectral matching tools indicates that, for better performance of target detection, there is a need for combining two or more spectral matching techniques. From the studies of several authors, it is inferred that continuous improvement in the matching techniques over the past few years is due to the need to handle and analyse hyperspectral image data for various applications. The need to develop a wellbuilt and specialized spectral library to accommodate the resources from enormous spectral data is suggested. This may improve accuracy in mineral and soil mapping, vegetation species identification and health monitoring, and target detection. The future role of cloud computing in accessing globally distributed spectral libraries and performing spectral matching is highlighted. Rather than inferring that a particular matching algorithm is the best, this paper points out the requirements of an ideal algorithm. With increasing usage of hyperspectral data for resources mapping, the review presented in this paper will certainly benefit the large and emerging community of hyperspectral image users.

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Overview of Hyperspectral Remote Sensing of Impervious Surfaces in Urban Environment

Deshpande¹,

Inamdar

Vin³

2013

Geo-Informatics in Resource Management and Sustainable Ecosystem

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Normalized Spectral Similarity Score (${\hbox{NS}}^{3}$) as an Efficient Spectral Library Searching Method for Hyperspectral Image Classification

Cited by 48 publications

References 25 publications

Quantification of cuttlefish (Sepia officinalis) camouflage: a study of color and luminance using in situ spectrometry

Quantification of cuttlefish (Sepia officinalis) camouflage: a study of color and luminance using in situ spectrometry

Spectral matching approaches in hyperspectral image processing

Overview of Hyperspectral Remote Sensing of Impervious Surfaces in Urban Environment

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