Superpixel Endmember Detection

David R. Thompson

doi:10.1109/TGRS.2010.2070802

2010-11-01

PRE

DOI：10.1109/TGRS.2010.2070802

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摘要

En 中文

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 the image. We then use segments' mean spectra as input to existing statistical endmember detection algorithms such as sequential maximum angle convex cone (SMACC) and N-FINDR. Experiments compare automatically detected endmembers to target minerals in an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene of Cuprite, Nevada. We also consider a planetary science data set from the Compact Reconnaissance Imaging Spectrometer (CRISM) instrument that benefits from spatial averaging due to higher noise. In both cases, superpixel representations significantly reduce the computational complexity of later processing while improving endmembers' match to the target spectra.

Keyword:

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

Compact Reconnaissance Imaging Spectrometer (CRISM)

endmember detection

hyperspectral images

planetary geology

segmentation

superpixels

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期刊

IEEE Transactions on Geoscience and Remote Sensing

IF:

8.6

论文数:

2.1W

被引数:

10.7W

机构

California Institute of Technology

学者数:

2.9W

论文数: 2.5W

被引数: 4.9W

national aeronautics & space administration (nasa)

学者数:

3.1W

论文数: 2.6W

被引数: 46

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