Adaptive wavelet classification of acoustic backscatter and imagery

Telfer, Brian A.; Szu, Harold; Dobeck, Gerald J.; Garcia, Joseph P.; Ko, Hanseok; Dubey, Abinash C.; Witherspoon, Ned H.

doi:10.1117/12.172257

Cited by 25 publications

(3 citation statements)

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“…9 WNN was utilized to design and apply wavelet classifiers into acoustic backscatter analysis, side-scan sonar, and multispectral electro-optical detection. 10 It was also used to optimize the coefficients of Gabor transform in order to better represent a two-dimensional (2D) image. 11 It was applied into low-order dominant harmonic estimations 12 and approximate arbitrary nonlinear functions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive wavelet transform: Definition, parameter optimization algorithms, and application for concrete delamination detection from impact echo responses

Chen

2018

Structural Health Monitoring

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In this study, an amplitude-adaptive wavelet transform is proposed and defined as an average of overlapped short-time wavelet transforms with optimized time-varying resolution in a time-frequency domain of interest. A flowchart of two optimization algorithms for time and frequency resolution updating is proposed and developed to determine the wavelet parameters for a desirable time-frequency representation of dynamic responses. Numerical example with two closely spaced, frequency-modulated sinusoids and two closely spaced delta functions indicates that the time and frequency resolution of the proposed method are approximately 7.7 times as uniform as those of the continuous wavelet transform. The end effect of short-time response segments is significantly less than 10% in amplitude except at the beginning and ending of the overall response. When the proposed method is applied to the impact echo responses experimentally recorded from a 60$ 3 36$ 3 7.25$ concrete slab, this improvement in time and frequency resolution leads to seven more successful detections of deep or shallow delamination of 40 sets of analyzed test data, with the error in estimation of deep delamination depth less than 3%. The selection process of time-varying central frequencies, scaling factors, and window lengths proves robust.

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Section: Introductionmentioning

confidence: 99%

Adaptive wavelet transform: Definition, parameter optimization algorithms, and application for concrete delamination detection from impact echo responses

Chen

2018

Structural Health Monitoring

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“…The test results indicated the effectiveness of the neuralnetwork-based detector/classifier systems. Telfer et al [8], [9] used wavelet preprocessing followed by neural-network detection/classification and obtained good results on multispectral imagery. Daud [10] used feedforward neural networks for mine discrimination using multispectral imagery.…”

Section: Introductionmentioning

confidence: 99%

Detection of mines and minelike targets using principal component and neural-network methods

Miao

Azimi-Sadjadi

Tan³

et al. 1998

IEEE Trans. Neural Netw.

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Abstract-This paper introduces a new system for real-time detection and classification of arbitrarily scattered surface-laid mines from multispectral imagery data of a minefield. The system consists of six channels which use various neural-network structures for feature extraction, detection, and classification of targets in six different optical bands ranging from near UV to near IR. A single-layer autoassociative network trained using the recursive least square (RLS) learning rule was employed in each channel to perform feature extraction. Based upon the extracted features, two different neural-network architectures were used and their performance was compared against the standard maximum likelihood (ML) classification scheme. The outputs of the detector/classifier network in all the channels were fused together in a final decision-making system. Two different final decision making schemes using the majority voting and weighted combination based on consensual theory were considered. Simulations were performed on real data for six bands and on several images in order to account for the variations in size, shape, and contrast of the targets and also the signalto-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets.

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“…Systems using wavelets for the purpose of classification and retrieval have been described in [6,10,13].…”

Section: Introductionmentioning

confidence: 99%

A linear shift invariant multiscale transform

Siebert

Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269)

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Adaptive wavelet classification of acoustic backscatter and imagery

Cited by 25 publications

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Adaptive wavelet transform: Definition, parameter optimization algorithms, and application for concrete delamination detection from impact echo responses

Adaptive wavelet transform: Definition, parameter optimization algorithms, and application for concrete delamination detection from impact echo responses

Detection of mines and minelike targets using principal component and neural-network methods

A linear shift invariant multiscale transform

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