DLCD Time Series Analysis

Submitted by Mueller Norman on Thu, 25/05/2017 - 16:07
Short Description

The MODIS EVI data cube is analysed to characterise the time series trends for each pixel. This process represents the time series in a form that allows it to be classified more easily. The initial characterisation of the time series data involves three steps:

(1) Piece-wise Linear Segmentation

The time series segmentation algorithm approximates a time series using a series of piece-wise linear segments. Each segment is characteristic of a sequence of environmental behaviour that typifies the associated land cover class.

(2) Sub-sequence Discretization

Each segment is then detailed by three values describing its starting value, slope and length to form a vector input to the classification process.

(3) Sequence Labelling

The sequences are then clustered into groups of like behaviour, and each group then assigned a behaviour label. A time series is thus transformed from a large collection of varying values to a smaller set of behaviour types, joined as a kind of “DNA” string representing the pixel through time.

(4) Markov modelling

The DNA string provides an initial class which is then modelled as a dynamic Markov Chain to estimate the likelihood that a particular land cover class would occur after any other land cover class.

(5) Bayesian classification

The final class is then assigned using a Bayesian classifier.