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We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use task-agnostic measures...
Spaceflight and planetary exploration place severe constraints on the available bandwidth for downlinking large hyperspectral images. In addition, communications with spacecraft often occur intermittently, so mission-relevant hyperspectral data must wait for analysis on the ground before it can inform spacecraft activity planning. Onboard endmember detection can help alleviate these problems. It enables...
This work presents an automated approach utilizing superpixel segmentation for detecting spectrally Neutral Regions (NR) in hyperspectral images. NRs are often used in planetary geology as spectral divisors to Regions of Interest (ROI), both to enhance key mineralogical signatures and correct for systematic errors such as residual atmospheric distortion. We compare automated NR selections to handpicked...
Superpixels are homogeneous image regions comprised of multiple contiguous pixels. Superpixel representations can reduce noise in hyperspectral images by exploiting the spatial contiguity of scene features. This paper combines superpixels with endmember extraction to produce concise mineralogical summaries that assist in browsing large image catalogs. First, a graph-based agglomerative algorithm oversegments...
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