A Generic Framework for Change Detection on Surface Water Bodies Using Landsat Time Series Data

2021 ◽  
pp. 303-314
Author(s):  
T. V. Bijeesh ◽  
K. N. Narasimhamurthy
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Change Detection ◽  
Time Series Data ◽  
Water Bodies ◽  
Series Data ◽  
Generic Framework ◽  
Surface Water Bodies

scholarly journals Transient dynamic phenotypes as criteria for model discrimination: fold-change detection in Rhodobacter sphaeroides chemotaxis

2013 ◽  
Vol 10 (80) ◽  
pp. 20120935 ◽  
Author(s):  
Abdullah Hamadeh ◽  
Brian Ingalls ◽  
Eduardo Sontag
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Rhodobacter Sphaeroides ◽  
Time Series Data ◽  
Series Data ◽  
Transient Dynamic ◽  
Experimental Time ◽  
Theoretical Assumptions ◽  
Experimental Time Series ◽  
Fold Change Detection

The chemotaxis pathway of the bacterium Rhodobacter sphaeroides shares many similarities with that of Escherichia coli . It exhibits robust adaptation and has several homologues of the latter's chemotaxis proteins. Recent theoretical results have correctly predicted that the E. coli output behaviour is unchanged under scaling of its ligand input signal; this property is known as fold-change detection (FCD). In the light of recent experimental results suggesting that R. sphaeroides may also show FCD, we present theoretical assumptions on the R. sphaeroides chemosensory dynamics that can be shown to yield FCD behaviour. Furthermore, it is shown that these assumptions make FCD a property of this system that is robust to structural and parametric variations in the chemotaxis pathway, in agreement with experimental results. We construct and examine models of the full chemotaxis pathway that satisfy these assumptions and reproduce experimental time-series data from earlier studies. We then propose experiments in which models satisfying our theoretical assumptions predict robust FCD behaviour where earlier models do not. In this way, we illustrate how transient dynamic phenotypes such as FCD can be used for the purposes of discriminating between models that reproduce the same experimental time-series data.

scholarly journals A METHOD OF TIME-SERIES CHANGE DETECTION USING FULL POLSAR IMAGES FROM DIFFERENT SENSORS

2018 ◽  
Vol XLII-3 ◽  
pp. 1127-1133
Author(s):  
W. Liu ◽  
J. Yang ◽  
J. Zhao ◽  
H. Shi ◽  
L. Yang
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Detection Methods ◽  
Series Data ◽  
Difference Image ◽  
Novel Method ◽  
Difference Images ◽  
The City

Most of the existing change detection methods using full polarimetric synthetic aperture radar (PolSAR) are limited to detecting change between two points in time. In this paper, a novel method was proposed to detect the change based on time-series data from different sensors. Firstly, the overall difference image of a time-series PolSAR was calculated by ominous statistic test. Secondly, difference images between any two images in different times ware acquired by R<sub>j</sub> statistic test. Generalized Gaussian mixture model (GGMM) was used to obtain time-series change detection maps in the last step for the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection by using the time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can detect the time-series change from different sensors.

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