Spectral gradient: a material descriptor invariant to geometry and incident illumination

Angelopoulou, Elli; Lee, S.W.; Bajcsy, Růžena

doi:10.1109/iccv.1999.790312

Cited by 32 publications

(23 citation statements)

References 21 publications

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“…In this paper we provide a conclusive methodology for obtaining a valuable clustering of multispectral or hyperspectral images under tight time constraints. First, we show that desirable results can be obtained by FAMS when applied on the spectral gradient descriptor [9]. Second, we adapt an established superpixel segmentation method to multispectral data.…”

Section: Introductionmentioning

confidence: 91%

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Mean-shift clustering for interactive multispectral image analysis

Jordan

Angelopoulou

2013

2013 IEEE International Conference on Image Processing

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Mean shift clustering and its recent variants are a viable and popular image segmentation tool. In this paper we investigate mean shift segmentation on multispectral and hyperspectral images and propose three new algorithms. First, we improve segmentation performance by running mean shift on the spectral gradient. At the same time, we adapt a popular superpixel segmentation method to the multispectral domain using modified similarity measures from spectral mapping. Based on superpixels, we design two mean shift variants that both obtain competitive segmentation results in significantly reduced running time. For one variant, the speedup in our benchmark is over 100 times. This enables mean shift clustering in an interactive setting.

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Section: Introductionmentioning

confidence: 91%

“…An appropriate representation for multispectral or hyperspectral data is the spectral gradient space [9]. The spectral gradient is the discrete approximation of spectral derivatives obtained by finite differencing.…”

Section: Spectral Gradient Fams (Sg-fams)mentioning

confidence: 99%

Mean-shift clustering for interactive multispectral image analysis

Jordan

Angelopoulou

2013

2013 IEEE International Conference on Image Processing

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“…The Spectral Gradient Angle (SGA) is the spectral angle between the derivative of the spectra (Angelopoulou et al, 1999).…”

Section: Parameters Choicementioning

confidence: 99%

A data-driven approach to quality assessment for hyperspectral systems

Kerr

Fischer

Reulke

2015

Computers & Geosciences

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“…In addition, correlation is used to produce a spectral measure, including crosscorrelation spectral measure [7] and spectral correlation mapper (SCM) [8], which is a derivative of Pearsonian correlation coefficient and can eliminate negative correlation and minimize the shading effect. Finally, spectral gradient angle (SGA) [9] is invariant to scene geometry and illumination conditions and takes into consideration slope changes within the vector in comparison with other methods. Meanwhile, Du et al [10] proposed two mixture measures, which are named StS/SsS, which considerably improved the spectral discriminability.…”

Section: Introductionmentioning

confidence: 99%

Spectral Similarity Measure Using Frequency Spectrum for Hyperspectral Image Classification

Wang

Yong

et al. 2015

IEEE Geosci. Remote Sensing Lett.

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A novel spectral similarity measure approach, which is named spectral frequency spectrum difference (SFSD), is proposed for hyperspectral image classification based on the frequency spectrum of spectral signature using the Fourier transform. Many important characteristics of spectral signature can be clearly reflected in the frequency spectrum. Therefore, the spectral similarity is defined as the frequency spectrum's difference between the target and reference signatures. The frequency spectrum analysis in this study suggests that the magnitude values of the first few low-frequency components for spectral signature can effectively represent the spectral similarity. To balance the difference between the low-and high-frequency components, the frequency spectrum of the target spectral signature is taken as the normalized factor in the SFSD method. Next, the U.S. Geological Survey spectral data and two hyperspectral remote sensing images were employed as test data in our validation experiments. The new SFSD proposed here was compared with the leading approaches in terms of the spectral discriminability and classification accuracy. Results show that the SFSD exhibits a relatively better performance and has more robust applications for hyperspectral image classification.

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Spectral gradient: a material descriptor invariant to geometry and incident illumination

Cited by 32 publications

References 21 publications

Mean-shift clustering for interactive multispectral image analysis

Mean-shift clustering for interactive multispectral image analysis

A data-driven approach to quality assessment for hyperspectral systems

Spectral Similarity Measure Using Frequency Spectrum for Hyperspectral Image Classification

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