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

Mei X, Ma Y, Li C, Fan F, Huang J, Ma J - Sensors (Basel) (2015)

Bottom Line: First, we introduce an infrared ultra-spectral signature similarity measure method via SPM, which is the foundation of the matching-based classification method.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.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China. meixiaoguang@hust.edu.cn.

ABSTRACT
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.

No MeSH data available.


Five spectra of compound 1,1-dimethyl hydrazine with different concentrations in the EPA dataset.
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f5-sensors-15-15868: Five spectra of compound 1,1-dimethyl hydrazine with different concentrations in the EPA dataset.

Mentions: In order to validate the feasibility of the proposed method, we conducted the experiments with 2 spectral libraries (the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) [43] and the Environmental Protection Agency (EAP) [44] spectral libraries), which are taken as the reference spectral libraries. The dimensions of spectra from ASTER and EPA spectral library are 42,861 and 32,000, respectively. There are 1432 types of materials in the ASTER dataset and 384 types of materials in the EPA dataset, respectively. Additionally, we evaluate our method on all of the spectral signatures contained in the datasets. Figure 4 shows the 5 spectra of solid man-made materials in the ASTER dataset, and Figure 5 shows the 5 spectra of compound 1,1-dimethyl hydrazine with different concentrations in the EPA dataset.


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

Mei X, Ma Y, Li C, Fan F, Huang J, Ma J - Sensors (Basel) (2015)

Five spectra of compound 1,1-dimethyl hydrazine with different concentrations in the EPA dataset.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541858&req=5

f5-sensors-15-15868: Five spectra of compound 1,1-dimethyl hydrazine with different concentrations in the EPA dataset.
Mentions: In order to validate the feasibility of the proposed method, we conducted the experiments with 2 spectral libraries (the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) [43] and the Environmental Protection Agency (EAP) [44] spectral libraries), which are taken as the reference spectral libraries. The dimensions of spectra from ASTER and EPA spectral library are 42,861 and 32,000, respectively. There are 1432 types of materials in the ASTER dataset and 384 types of materials in the EPA dataset, respectively. Additionally, we evaluate our method on all of the spectral signatures contained in the datasets. Figure 4 shows the 5 spectra of solid man-made materials in the ASTER dataset, and Figure 5 shows the 5 spectra of compound 1,1-dimethyl hydrazine with different concentrations in the EPA dataset.

Bottom Line: First, we introduce an infrared ultra-spectral signature similarity measure method via SPM, which is the foundation of the matching-based classification method.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.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China. meixiaoguang@hust.edu.cn.

ABSTRACT
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.

No MeSH data available.