Open boundary dynamic time warping for satellite image time series classification

2015 ◽  
Author(s):  
Victor Maus ◽  
Gilberto Camara ◽  
Ricardo Cartaxo ◽  
Fernando M. Ramos ◽  
Alber Sanchez ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Satellite Image ◽  
Open Boundary ◽  
Time Series Classification ◽  
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.

Weighted dynamic time warping for time series classification

2011 ◽  
Vol 44 (9) ◽  
pp. 2231-2240 ◽  
Author(s):  
Young-Seon Jeong ◽  
Myong K. Jeong ◽  
Olufemi A. Omitaomu
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Time Series Classification ◽  
Time Warping ◽  
Dynamic Time
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