Satellite image band registration with Dynamic Time Warping and Discrete Wavelet Transform

2018 ◽  
Author(s):  
D. Utku Ufuk ◽  
Ibrahim S. Acikgoz ◽  
Mustafa Teke ◽  
A. Murat Ozbayoglu
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Dynamic Time Warping ◽  
Satellite Image ◽  
Discrete Wavelet ◽  
Time Warping ◽  
Dynamic Time ◽  
Image Band

Improved Dynamic Time Warping Based on the Discrete Wavelet Transform

2007 ◽  
Author(s):  
Sylvio Barbon Junior ◽  
Rodrigo Capobianco Guido ◽  
Shi-Huang Chen ◽  
Lucimar Sasso Vieira ◽  
Fabricio Lopes Sanchez
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Dynamic Time Warping ◽  
Discrete Wavelet ◽  
Time Warping ◽  
Dynamic Time

SPOKEN DOCUMENT SUMMARIZATION BASED ON DYNAMIC TIME WARPING AND WAVELETS

2007 ◽  
Vol 01 (03) ◽  
pp. 347-357 ◽  
Author(s):  
RODRIGO CAPOBIANCO GUIDO ◽  
SYLVIO BARBON JUNIOR ◽  
LUCIMAR SASSO VIEIRA ◽  
FABRÍCIO LOPES SANCHEZ ◽  
CARLOS DIAS MACIEL ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Dynamic Time Warping ◽  
Discrete Wavelet ◽  
Time Warping ◽  
Speech Corpus ◽  
Document Summarization ◽  
Dynamic Time

This work presents a spoken document summarization (SDS) scheme that is based on an improved version of the Dynamic Time Warping (DTW) algorithm, and on the Discrete Wavelet Transform (DWT). Tests and results with sentences extracted from TIMIT speech corpus show the efficacy of the proposed technique.

Improved Dynamic Time Warping Based on the Discrete Wavelet Transform

2007 ◽  
Author(s):  
Sylvio Barbon Junior ◽  
Rodrigo Capobianco Guido ◽  
Shi-Huang Chen ◽  
Lucimar Sasso Vieira ◽  
Fabricio Lopes Sanchez
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Dynamic Time Warping ◽  
Discrete Wavelet ◽  
Time Warping ◽  
Dynamic Time

scholarly journals Satellite Image Time Series Clustering via Time Adaptive Optimal Transport

2021 ◽  
Vol 13 (19) ◽  
pp. 3993
Author(s):  
Zheng Zhang ◽  
Ping Tang ◽  
Weixiong Zhang ◽  
Liang Tang
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Dynamic Time Warping ◽  
Optimal Transport ◽  
Satellite Image ◽  
Time Warping ◽  
Unsupervised Analysis ◽  
Training Samples ◽  
Dynamic Time ◽  
Visual Results

Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.

On-Line Signature Verification Utilizing 3-Axis Force Features Extraction Based on DWT

2012 ◽  
Vol 241-244 ◽  
pp. 2990-2995
Author(s):  
Dong Liu ◽  
Yun Jian Ge ◽  
Xue Yong Zhang
Keyword(s):  
Wavelet Transform ◽  
Signature Verification ◽  
Discrete Wavelet ◽  
Dynamic Features ◽  
Time Warping ◽  
Practical Applications ◽  
Location Verification ◽  
Multiple Thresholds ◽  
On Line ◽  
Dynamic Time

Automatic on-line signature verification is an intriguing intellectual challenge with wide attention and practical applications. We examined the validation of the new dynamic features for on-line signature verification, which extracted from 3-axis forces of pen-tip to writing tablet with application of discrete wavelet transform (DWT). The force signals were decomposed into sub-band signals by DWT and individual features were extracted as locations of modulus maximum in detail and the values of the wavelet transform at the corresponding location. Verification was achieved by using dynamic time warping (DTW) and total decision was done by combining multiple thresholds. Experimental results indicate the effectiveness of the dynamic features for on-line signature verification described in this paper.

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