Spectral derivative feature coding for hyperspectral signature analysis

Chang, Chein‐I; Chakravarty, Sumit; Chen, Hsian‐Min; Ouyang, Yen-Chieh

doi:10.1016/j.patcog.2008.07.016

Cited by 30 publications

(9 citation statements)

References 6 publications

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“…The four compared methods are as follows: BC [ 26 ], SFBC [ 29 ], SDFC [ 25 ] and CF [ 13 ]. According to the experiments in [ 13 ], M = 20, s = 20 are validated as the optimal settings.…”

Section: Methodsmentioning

confidence: 99%

“…Over the past years, two general approaches have been investigated for hyperspectral signature characterization [ 25 ]. The first is a coding-based approach, which encodes spectral signatures as code words.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, it cannot classify spectra within the same class, due to the homogeneity of spectral signatures in the same class [ 27 ]. To address these problems, other algorithms were proposed, for example the spectral analysis manager (SPAM) [ 28 ], the spectral feature-based binary coding (SFBC) [ 29 ], the spectral derivative feature coding (SDFC) [ 25 ], etc . All of these algorithms use extra bits to encode spectral features, such as spectral derivatives and neighboring waveband differences, which can carry more spectral information.…”

Section: Related Workmentioning

confidence: 99%

“…All of these algorithms use extra bits to encode spectral features, such as spectral derivatives and neighboring waveband differences, which can carry more spectral information. The second type of approach is a signature estimation-based approach, which estimates spectral profiles for signatures, and spectral analysis is then carried out by using the commonly-used least squares error as a criterion for optimality, such as methods based on wavelets and the Kalman filter [ 25 ]. The above-mentioned two kinds of methods have shown their good performance in multispectral or even hyperspectral applications.…”

Section: Related Workmentioning

confidence: 99%

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A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching

Mei

et al. 2015

Sensors

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The state-of-the-art ultra-spectral sensor technology brings new hope for high precision applications due to its high spectral resolution. However, it also comes with new challenges, such as the high data dimension and noise problems. In this paper, we propose a real-time method for infrared ultra-spectral signature classification via spatial pyramid matching (SPM), which includes two aspects. First, we introduce an infrared ultra-spectral signature similarity measure method via SPM, which is the foundation of the matching-based classification method. Second, we propose the classification method with reference spectral libraries, which utilizes the SPM-based similarity for the real-time infrared ultra-spectral signature classification with robustness performance. Specifically, instead of matching with each spectrum in the spectral library, our method is based on feature matching, which includes a feature library-generating phase. We calculate the SPM-based similarity between the feature of the spectrum and that of each spectrum of the reference feature library, then take the class index of the corresponding spectrum having the maximum similarity as the final result. Experimental comparisons on two publicly-available datasets demonstrate that the proposed method effectively improves the real-time classification performance and robustness to noise.

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“…The four compared methods are as follows: BC [ 26 ], SFBC [ 29 ], SDFC [ 25 ] and CF [ 13 ]. According to the experiments in [ 13 ], M = 20, s = 20 are validated as the optimal settings.…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching

Mei

et al. 2015

Sensors

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show abstract

“…Robles-Kelly, C.P.Huynh, Imaging Spectroscopy for Scene Analysis, Advances in Computer Vision and Pattern Recognition, DOI 10.1007/978-1-4471-4652-0_7, © Springer-Verlag London 2013 89Chang et al (2009) developed a coding scheme for the spectral variation of hyperspectral signatures by means of spectral derivatives.…”

mentioning

confidence: 99%