isofit.surface.surface_multicomp

class MultiComponentSurface(full_config)

Bases: Surface

A model of the surface based on a collection of multivariate Gaussians, with one or more equiprobable components and full covariance matrices.

To evaluate the probability of a new spectrum, we calculate the Mahalanobis distance to each component cluster, and use that as our Multivariate Gaussian surface model.

component(x, geom)

We pick a surface model component using the Mahalanobis distance.

This always uses the Lambertian (non-specular) version of the surface reflectance. If the forward model initialize via heuristic (i.e. algebraic inversion), the component is only calculated once based on that first solution. That state is preserved in the geometry object.

xa(x_surface, geom)

Mean of prior distribution, calculated at state x. We find the covariance in a normalized space (normalizing by z) and then un- normalize the result for the calling function. This always uses the Lambertian (non-specular) version of the surface reflectance.

Sa(x_surface, geom)

Covariance of prior distribution, calculated at state x. We find the covariance in a normalized space (normalizing by z) and then un- normalize the result for the calling function.

fit_params(rfl_meas, geom, *args)

Given a reflectance estimate, fit a state vector.

calc_rfl(x_surface, geom)

Non-Lambertian reflectance.

calc_lamb(x_surface, geom)

Lambertian reflectance.

drfl_dsurface(x_surface, geom)

Partial derivative of reflectance with respect to state vector, calculated at x_surface.

dlamb_dsurface(x_surface, geom)

Partial derivative of Lambertian reflectance with respect to state vector, calculated at x_surface.

calc_Ls(x_surface, geom)

Emission of surface, as a radiance.

dLs_dsurface(x_surface, geom)

Partial derivative of surface emission with respect to state vector, calculated at x_surface.

summarize(x_surface, geom)

Summary of state vector.