SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS
Description
SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS
MONITORING USING GAUSSIAN PROCESS
VARUN CHANDOLA* AND RANGA RAJU VATSAVAI*
Abstract. Biomass monitoring, specifically, detecting changes in the biomass or vegetation of
a geographical region, is vital for studying the carbon cycle of the system and has significant
implications in the context of understanding climate change and its impacts. Recently, several time
series change detection methods have been proposed to identify land cover changes in temporal
profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we
adapt Gaussian process regression to detect changes in such time series in an online fashion. While
Gaussian process (GP) has been widely used as a kernel based learning method for regression and
classification, their applicability to massive spatio-temporal data sets, such as remote sensing data,
has been limited owing to the high computational costs involved. In our previous work we proposed
an efficient Toeplitz matrix based solution for scalable GP parameter estimation. In this paper we
apply these solutions to a GP based change detection algorithm. The proposed change detection
algorithm requires a memory footprint which is linear in the length of the input time series and
runs in time which is quadratic to the length of the input time series. Experimental results show
that both serial and parallel implementations of our proposed method achieve significant speedups
over the serial implementation. Finally, we demonstrate the effectiveness of the proposed change
detection method in identifying changes in Normalized Difference Vegetation Index (NDVI) data.
Resources
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SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS |
https://c3.nasa.gov/dashlink/static/media/publication/Paper_6_.pdf |