Assessment and robust reconstruction of laser radar signals

Rothe, Hendrik; Ginter, Olaf; Woldenga, C.

doi:10.1016/0030-3992(93)90016-9

Cited by 12 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…After identifying these spatial-temporal structures, four different subsets of frames are produced by the rectification procedure [5,8]. The first one reproduces the frames with only FPN and stationary principal components.…”

Section: Classification Of Spatial-temporal Processesmentioning

confidence: 99%

Characterization of dynamic sea scenarios with infrared imagers

López-Alonso

Alda

2005

Infrared Physics & Technology

View full text Add to dashboard Cite

Section: Classification Of Spatial-temporal Processesmentioning

confidence: 99%

Characterization of dynamic sea scenarios with infrared imagers

López-Alonso

Alda

2005

Infrared Physics & Technology

View full text Add to dashboard Cite

“…The principal-component expansion corresponds with new variables, obtained as a linear combination of the original ones, that do not present covariance among them. [3][4][5][6] In addition, the variance of these new variables is arranged in decreasing order. Mathematically, this expansion is obtained by the diagonalization of the S matrix that produces a set of eigenvalues, ␣ , and eigenvectors, E ␣ .…”

Section: Principal-component Expansionmentioning

confidence: 99%

“…The operational method for answering this question is called "rectification" or "principal component filtering." 5 It works by building a rectification matrix obtained by the following equation,…”

Section: B Fixed-pattern Noise and Pure Temporal Noisementioning

confidence: 99%

Principal-component characterization of noise for infrared images

López-Alonso¹,

Alda²,

Bernabéu³

2002

Appl. Opt.

100

View full text Add to dashboard Cite

Principal-component decomposition is applied to the analysis of noise for infrared images. It provides a set of eigenimages, the principal components, that represents spatial patterns associated with different types of noise. We provide a method to classify the principal components into processes that explain a given amount of the variance of the images under analysis. Each process can reconstruct the set of data, thus allowing a calculation of the weight of the given process in the total noise. The method is successfully applied to an actual set of infrared images. The extension of the method to images in the visible spectrum is possible and would provide similar results.

show abstract

“…Then, it is possible to retrieve the original frames with only a subset of principal components what is called "principal components rectification", or, what is the same, to filter the complete whole of data to extract features or interest directly related with some specific components 6,7 (see Figure 1). Besides the eigenimages , the method gives two other parameters, eigenvalues and eigenvectors.…”

Section: Principal Components Expansionsmentioning

confidence: 99%

“…This question will be addressed in the following subsection. It is important to know that for a practical case equation (6), applied to harmonic eigenfunctions, is: [12] where the delta function of equation (6) has been replaced by the sinc function in [12]. Then, when at least the Fourier Transform of ) (t f is as narrow as the sinc function, it could be considered practically as a delta function and then the eigenvectors given by the PCA are harmonic functions and the eigenvalues associated can be considered as a sample of the PSD of the noise.…”

Section: 1mentioning

confidence: 99%

Automatic classification for noise of infrared images into processes by means of the principal component analysis

López-Alonso

Alda

2002

SPIE Proceedings

View full text Add to dashboard Cite

Noise characterization and classification is an important task to evaluate the performance o f an infrared imaging system. The focal plane array infrared cameras present several types of noises: fixed pattern noise, 1/f noise, pure temporal noise, etc. The existence of bad pixels showing a singular behavior must be included in the noise description. In this paper we show how the principal component analysis is able to classify the noise of a set of frames into different subsets. The classification method is integrated into a software package that performs the classification of the obtained eigenimages into processes. This method is specially adapted to the analysis of noise in a set of frames because it produces a corresponding set of images characterizing the noise. A result of the analysis provided with this method is the extraction of the fixed pattern noise, the bad pixel identification, the 1/f noise components and analysis, the pure temporal noise, and some other processes having intermediate time scales.

show abstract

Assessment and robust reconstruction of laser radar signals

Cited by 12 publications

References 15 publications

Characterization of dynamic sea scenarios with infrared imagers

Characterization of dynamic sea scenarios with infrared imagers

Principal-component characterization of noise for infrared images

Automatic classification for noise of infrared images into processes by means of the principal component analysis

Contact Info

Product

Resources

About