The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery

Meer, Freek D. van der

doi:10.1016/j.jag.2005.06.001

Cited by 322 publications

(183 citation statements)

References 10 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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“…For this purpose, power of spectral discrimination (PWSD) is utilized, which was proposed in [30]. PWSD provides an assessment criterion depending on two reference cluster centers for a particular pixel.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Integration of spectral and spatial information via local covariance matrices for segmentation and classification of hyperspectral images

Ergül¹,

Bilgin²

2016

Turk J Elec Eng & Comp Sci

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Abstract:In this work, a novel approach is presented for the feature extraction step in hyperspectral image processing to form more discriminative features between different pixel regions. The proposed method combines both spatial and spectral information, which is very important for segmentation and classification of hyperspectral images. For comparison, five different feature sets are formed using eigen decomposition of local covariance matrices of subcubes located around a pixel of interest in the scene. Subcubes of neighbor pixels are obtained by a windowed structure to expose pattern similarities. As a novel approach, local covariance matrices are computed in eigenspace and proposed feature sets are created after this stage. Before the formation of feature sets in eigenspace, the original input space is transferred to eigenspace by linear and nonlinear manner by principal component analysis (PCA) and its kernelized version (KPCA) and they are used in the experiments comparatively. In the simulations, one hyperspectral scene with ground-truth and one without ground-truth are used for the segmentation and classification tasks. Results of experiments are evaluated with four different unsupervised learning algorithms for data without ground-truth and three different supervised learning algorithms for data with ground-truth comparatively.

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“…Heiden et al [4] numerically describe the spectral reflectance curve using various feature functions, such as mean, standard deviation and ratios of the reflectance between two wavelengths, by the area under the feature in the spectrum, and by the absorption depth and position. Spectral matching techniques assess the similarity of an image spectrum to a known class spectrum [125,126]. Examples of similarity measures are spectral information divergence (SID) [127], Euclidean distance (ED) or spectral angle mapper (SAM) [128].…”

Section: Hyperspectral Approachesmentioning

confidence: 99%

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

et al. 2011

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With urban populations and their footprints growing globally, the need to assess the dynamics of the urban environment increases. Remote sensing is one approach that can analyze these developments quantitatively with respect to spatially and temporally large scale changes. With the 2015 launch of the spaceborne EnMAP mission, a new hyperspectral sensor with high signal-to-noise ratio at medium spatial resolution, and a 21 day global revisit capability will become available. This paper presents the results of a literature survey on existing applications and image analysis techniques in the context of urban remote sensing in order to identify and outline potential contributions of the future EnMAP mission. Regarding urban applications, four frequently addressed topics have been identified: urban development and planning, urban growth assessment, risk and vulnerability assessment and urban climate. The requirements of four application fields and associated image processing techniques used to retrieve desired parameters and create geo-information products have been reviewed. As a result, we identified promising research directions enabling the use of EnMAP for urban studies. First and foremost, research is required to analyze the spectral information content of an EnMAP pixel used to support material-based land cover mapping approaches. This information can subsequently be used to improve urban indicators, such as imperviousness. Second, we identified the global monitoring of urban areas as a promising field of investigation taking advantage of EnMAP's spatial coverage and revisit capability.Remote Sens. 2011, 3 1818However, owing to the limitations of EnMAPs spatial resolution for urban applications, research should also focus on hyperspectral resolution enhancement to enable retrieving material information on sub-pixel level.

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The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery

Cited by 322 publications

References 10 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

Integration of spectral and spatial information via local covariance matrices for segmentation and classification of hyperspectral images

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

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