307 research outputs found
Hyperspectral unmixing with material variability using social sparsity
International audienceWe apply social-norms for the first time to the problem of hyperspectral unmixing while modeling spectral variability. These norms are built with inter-group penalties which are combined in a global intra-group penalization that can enforce selection of entire endmember bundles; this results in the selection of a few representative materials even in the presence of large endmembers bundles capturing each material's variability. We demonstrate improvements quantitatively on synthetic data and qualitatively on real data for three cases of social norms: group, elitist, and a fractional social norm, respectively. We find that the greatest improvements arise from using either the group or fractional flavor
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness
In recent hyperspectral unmixing (HU) literature, the application of deep
learning (DL) has become more prominent, especially with the autoencoder (AE)
architecture. We propose a split architecture and use a pseudo-ground truth for
abundances to guide the `unmixing network' (UN) optimization. Preceding the UN,
an `approximation network' (AN) is proposed, which will improve the association
between the centre pixel and its neighbourhood. Hence, it will accentuate
spatial correlation in the abundances as its output is the input to the UN and
the reference for the `mixing network' (MN). In the Guided Encoder-Decoder
Architecture for Hyperspectral Unmixing with Spatial Smoothness (GAUSS), we
proposed using one-hot encoded abundances as the pseudo-ground truth to guide
the UN; computed using the k-means algorithm to exclude the use of prior HU
methods. Furthermore, we release the single-layer constraint on MN by
introducing the UN generated abundances in contrast to the standard AE for HU.
Secondly, we experimented with two modifications on the pre-trained network
using the GAUSS method. In GAUSS, we have concatenated the UN
and the MN to back-propagate the reconstruction error gradients to the encoder.
Then, in the GAUSS, abundance results of a signal processing
(SP) method with reliable abundance results were used as the pseudo-ground
truth with the GAUSS architecture. According to quantitative and graphical
results for four experimental datasets, the three architectures either
transcended or equated the performance of existing HU algorithms from both DL
and SP domains.Comment: 16 pages, 6 figure
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