Online unmixing of multi-temporal hyperspectral images
Hyperspectral unmixing is aimed at identifying the
reference spectral signatures composing an hyperspectral image
and their relative abundance fractions in each pixel. In practice,
the identified signatures may vary spectrally from an image
to another due to varying acquisition conditions, thus inducing
possibly significant estimation errors. Against this background,
hyperspectral unmixing of several images acquired over the same
area is of considerable interest. Indeed, such an analysis enables
the endmembers of the scene to be tracked and the corresponding
endmember variability to be characterized. Sequential
endmember estimation from a set of hyperspectral images is
expected to provide improved performance when compared
to methods analyzing the images independently. However, the
significant size of hyperspectral data precludes the use of batch
procedures to jointly estimate the mixture parameters of a
sequence of hyperspectral images. Provided that each elementary
component is present in at least one image of the sequence, we
propose to perform an online hyperspectral unmixing accounting
for temporal endmember variability. The online hyperspectral
unmixing is formulated as a two-stage stochastic program, which
can be solved using a stochastic approximation. The performance
of the proposed method is evaluated on synthetic and real data.
A comparison with independent unmixing algorithms finally
illustrates the interest of the proposed strategy.
Fig. 1. Abundance maps of the third endmember used in the synthetic mixtures (theoretical abundances on the first line, VCA/FCLS on the second line, proposed method on the third line). The top line indicates the theoretical maximum abundance value and the true number of pixels whose abundance is greater than 0.95 for each time instant.
The associated unmixing algorithm is described in the paper published in IEEE Trans. Image Processing in 2016:
- article
[ .pdf - 6.4Mo ].
The PLMM-based online unmixing algorithm is available as a MATLAB code:
- matlab codes
[ .zip - 73.0Mo ].
