An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing
arxiv(2024)
摘要
Spectral Unmixing is an important technique in remote sensing used to analyze
hyperspectral images to identify endmembers and estimate abundance maps. Over
the past few decades, performance of techniques for endmember extraction and
fractional abundance map estimation have significantly improved. This article
presents an ensemble model workflow called Autoencoder Graph Ensemble Model
(AEGEM) designed to extract endmembers and fractional abundance maps. An
elliptical kernel is applied to measure spectral distances, generating the
adjacency matrix within the elliptical neighborhood. This information is used
to construct an elliptical graph, with centroids as senders and remaining
pixels within the geometry as receivers. The next step involves stacking
abundance maps, senders, and receivers as inputs to a Graph Convolutional
Network, which processes this input to refine abundance maps. Finally, an
ensemble decision-making process determines the best abundance maps based on
root mean square error metric. The proposed AEGEM is assessed with benchmark
datasets such as Samson, Jasper, and Urban, outperforming results obtained by
baseline algorithms. For the Samson dataset, AEGEM excels in three abundance
maps: water, tree and soil yielding values of 0.081, 0.158, and 0.182,
respectively. For the Jasper dataset, results are improved for the tree and
water endmembers with values of 0.035 and 0.060 in that order, as well as for
the mean average of the spectral angle distance metric 0.109. For the Urban
dataset, AEGEM outperforms previous results for the abundance maps of roof and
asphalt, achieving values of 0.135 and 0.240, respectively. Additionally, for
the endmembers of grass and roof, AEGEM achieves values of 0.063 and 0.094.
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