Table 1 A summary of the selected decomposition algorithms and their parameters
Algorithm | Parameter (λ or α) | Domain | Description |
|---|---|---|---|
sPCA | λ = 150 | Spatial | Extension of principal component analysis (PCA) by sparse constraint, i.e., uses L1 penalty on the spatial loadings in the optimization procedure. λ denotes the number of non-zero pixels, a parameter equal to 150 and 250 corresponds to territories with 4.3 and 5.6 mm in diameter |
λ = 250 | Spatial | ||
stICA | α = 0.0 | Temporal | Separation by optimizing a joint entropy energy function based on mutual entropy and infomax with a kurtosis-based cost function. α = 0.8 has been used previously [11, 12], i.e., weighs 0.8 in spatial and 0.2 in temporal separation |
α = 0.8 | Spatiotemporal | ||
α = 1.0 | Spatial | ||
stJADE | α = 0.0 | Temporal | Joint diagonalization of fourth-order cumulant tensor in separation procedure. A low α weighs more on temporal separation |
α = 0.5 | Spatiotemporal | ||
α = 1.0 | Spatial | ||
stSOBI | α = 0.0 | Temporal | Autocovariance matrices (fixed number, 12) for joint diagonalization of a set of symmetrized multidimensional autocovariances [28, 29]. Similar to stJADE, a low α weighs more on temporal separation |
α = 0.5 | Spatiotemporal | ||
α = 1.0 | Spatial |