Automatic Cross-calibration of Multispectral Imagery with Airborne Hyperspectral Imagery Using Spectral Mixture Analysis

Yeji, Kim,Jaewan, Choi,Anjin, Chang,Yongil, Kim Korean Society of Surveying 2015 한국측량학회지 Vol.33 No.3

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

Automatic Cross-calibration of Multispectral Imagery with Airborne Hyperspectral Imagery Using Spectral Mixture Analysis

김예지,최재완,장안진,김용일 한국측량학회 2015 한국측량학회지 Vol.33 No.3

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

당뇨병 환자의 자율신경병증 평가에 있어 24 시간 Holter Monitoring 을 이용한 Power Spectrum 의 Diurnal Variation 에 대한 연구

박순희(Sun Hee Park),박종욱(Chong Wook Park),김정식(Jeong Sik Kim),곽현철(Hyun Cheol Kwak),김광석(Kwang Suck Kim),오성주,김수길,원동준(Dong Jun Won),박정식(Jeong Sik Park) 대한내과학회 1997 대한내과학회지 Vol.53 No.4

N/A Objective: We studied the efficacy of the power spectral and nonspectral analysis and its diurnal variations for the early detection of the diabetic autonomic neuropathy. Method: The spectral and nonspectral analysis of 24hour-Holter monitoring were done for both diabetic neuropathy patients and controls. We also made a comparative analysis of the diurnal variations between the patient and control groups by means of hourly power spectral analysis. Result: 1) The power spectral density of the diabetic neuropathy patients was below than that of the normal controls (P<0.05) (Table 2, Fig. 1). 2) The nonspectral analysis of the diabetic neuropathy patients was below than that of the normal controls except for the mean RR intervals (P<0.05) (Table 3). 3) Every hour-power spectral analysis showed a diurnal variation of day time (06:00 to 16:00) decrease in high frequency area of the normal controls. On the contrary, there was a disappearance of the diurnal variation in patient group. Conclusion: The power spectral and nonspectral analyses after 24hour-Holter monitoring and its diurnal variation in the diabetic patients are considered as good means of the early detection of autonomic neuropathy, but further study of its diagnostic value will be needed.

Spectral/cepstral analyses of connected speech in parkinson’s disease as compared with sustained phonation before and after voice treatment

Ghadah G. Alharbi,Michael P. Cannito,Eugene H. Buder,Shaheen N. Awan 한국언어재활사협회 2021 Clinical Archives of Communication Disorders Vol.6 No.2

Purpose: The primary purpose of this study was to assess the effect of Lee Silverman Voice Treatment (LSVT®LOUD) on selected spectral/cepstral measures of voice in connected speech. Spectral/cepstral analyses also were used to descriptively compare changes in connected speech to those previously reported in sustained vowels. An additional goal was to examine individual differences in responses to LSVT across the spectral/cepstral measures. Methods: Nine adult participants with PD were examined in a pre/post treatment design. Speech recordings were obtained on three different days within one week before and one week after LSVT. Speech recordings were analyzed for cepstral peak prominence (CPP), CPP Standard Deviation (CPP-SD), Low/High Spectral Ratio (L/H SR), and Cepstral/Spectral Index of Dysphonia (CSID) using the Analysis of Dysphonia in Speech and Voice (ADSV) program. Results: CPP and CSID were the only measures that detect treatment-related changes in connected speech. Investigating individual differences demonstrated more participants exhibiting an improvement in sustained phonation than in connected speech. Conclusions: Cepstral/spectral measures have been shown to be valid measures for estimating dysphonia severity in both continuous speech and sustained vowels. In addition, it extends the use of the cepstral/spectral measures for characterizing speech and voice aspects prior to treatment and for quantifying treatment outcomes. Moreover, it supports the use of LSVT as a treatment approach for improving voice quality in addition to intensity in PD.

Projection spectral analysis: A unified approach to PCA and ICA with incremental learning

강훈,이현수 한국전자통신연구원 2018 ETRI Journal Vol.40 No.5

Projection spectral analysis is investigated and refined in this paper, in order to unify principal component analysis and independent component analysis. Singular value decomposition and spectral theorems are applied to nonsymmetric correlation or covariance matrices with multiplicities or singularities, where projections and nilpotents are obtained. Therefore, the suggested approach not only utilizes a sum‐product of orthogonal projection operators and real distinct eigenvalues for squared singular values, but also reduces the dimension of correlation or covariance if there are multiple zero eigenvalues. Moreover, incremental learning strategies of projection spectral analysis are also suggested to improve the performance.

Projection Spectral Analysis

강훈,하준수 제어·로봇·시스템학회 2015 International Journal of Control, Automation, and Vol.13 No.6

This study investigates ‘Projection Spectral Analysis’, which generalizes ‘Principal or Independent Component Analysis’ by dealing with a non-symmetric square correlation or covariance matrix with multiplicities or singularities. This type of covariance matrix is decomposed into projections and nilpotents according to the spectral theorem. Projection spectral analysis solves a learning problem by reducing the dimension for multiple zero eigenvalues, and may be applied to a non-symmetric co-variance with distinct eigenvalues. This method involves a sum-product of orthogonal projection operators and real distinct eigenvalues for a symmetric covariance, which makes it equivalent to principal component analysis. However, it becomes independent component analysis if the covariance is not symmetric.

정상뇌파의 스펙트럼 분석

최경규 대한신경과학회 1999 대한신경과학회지 Vol.17 No.3

A Study on the Enhancement of Voice Match Based on Spectral-Contents Analysis

Hangil Kim,Kiman Jang,Hoe-Kyung Jung 한국정보통신학회 2015 2016 INTERNATIONAL CONFERENCE Vol.7 No.1

Researches on voice analysis have been in progress to verify the integrity of the original audio data. However, the spectrogram currently used or the technique for analyzing the frequency waveform has a problem of low accuracy in determining the voice match. In this paper, through voice analysis methods using the spectral content, we propose a method that can be performed and prove it by analyzing each individual’s unique audio-frequency shape and determining whether the voice is matched. Results for voice analysis and matching determination are to present a solution to voice-related disputes and crimes. In the IT industry, they are considered to have a wide range of applications in technology using a speech recognition device or a smart security device.

개선된 ARMA FTF 알고리즘을 이용한 ECG 신호의 스펙트럼 해석

남현도,안동준,이철희,Nam, Hyeon-Do,An, Dong-Jun,Lee, Cheol-Hui 대한의용생체공학회 1994 의공학회지 Vol.15 No.4

High resolution spectral analysis is essential for ECG anaysis. The fast Fourier transform has been widely used for frequency analysis of ECG signals but this procedure provides poor resolution when the data record is short and shows Gibb's phenomena. The ARMA FTF (Fast Transversal Filter) algorithm is used for high resolution spectral analysis. The reason of unsalability of this algorithm is investigated and the method for improving the numerical stability is proposed. The proposed algorithm is applied to spectral analysis of the ECG. Since this result has less variations than the FFT based results, it can be used for the computerized diagonosis of the ECG. ECG 신호해석을 위해서는 고해상도 스펙트럼 해석이 필수적이다. ECG 신호의 주파수 해석을 위하여 많이 사용된고 있는 FFT(Fast Fourier Transform)는 데이타 수가 적을때는 해상도가 나쁘고 Gibb의 현상을 보인다. ARMA 모델을 이용한 고해상도 스펙트럼 해석을 위하여 ARMA FTF(Fast Transversal Filter)알고리즘을 사용하였으며 ARMA FTF알고리즘의 불안정의 원인을 분석하고 이의 개선책을 제시하였다. 제안된 알고리즘을 사용하여 ECG 신호의 스펙트럼 해석에 적용하여 좋은 결과를 얻었다. 본 연구의 결과는 FFT를 사용한 결과보다 굴곡이 작아 컴퓨터를 이용한 진단에 유용하게 사용될 수 있으리라 생각된다.

특징형상 제약조건을 이용한 메쉬 모델의 스펙트럼 분석

최한균(Han Kyun Choi),이승주(Seung Joo Lee),박민기(Min Ki Park),이관행(Kwan H. Lee) (사)한국CDE학회 2011 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2011 No.1

The spectral analysis of the mesh models is widely used in CAD and computer graphics area. Especially, the importance of it is continuously increasing to find fundamental bases of the objects at various applications such as parameterization, geometric filtering, shape analysis and correspondence. The spectral analysis of two-manifold objects can be conducted by eigendecomposition of the Laplacian matrix in the discrete geometry. The positive square roots of the eigenvalues are corresponding to the frequency of the target objects. Consequently, the geometric filtering can be achieved by controlling the weights of eigenvalues when the Laplacian matrix is reconstructed by the user specified number of eigenvalues. In this paper, we find salient features of the models then they are used as the constraints for the spectral analysis.