Application of high spatial resolution airborne hyperspectral remote sensing data in thematic information extraction

Hong-gen Xu; Hong-chao Ma; De-ren Li; Yan Song

doi:10.1117/12.712738

28 October 2006 Application of high spatial resolution airborne hyperspectral remote sensing data in thematic information extraction

Hong-gen Xu, Hong-chao Ma, De-ren Li, Yan Song

Author Affiliations +

Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64190H (2006) https://doi.org/10.1117/12.712738
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China

Abstract

The airborne hyperspectral remote sensing data, such as PHI, OMIS, has the virtues of high spatial and spectral resolution. Hence, from the view of target classification we can consider that it can provide the ability of discriminating targets more detailedly than other data. So it's important to extract thematic information and update database using this kind of data. Whereas, the hyperspectral data has abundant bands and high between-band correlation, the traditional classification methods such as maximum likelihood classifier (MLC) and spectral angle mapper (SAM) have performed poorly in thematic information extraction. For this reason, we present a new method for thematic information extraction with hyperspectral remote sensing data. We perform classification by means of combining the self-organizing map (SOM) neural network which is considered as full-pixel technique with linear spectral mixture analysis (LSMA) which is considered as mixed-pixel technique. The SOM neural network is improved from some aspects to classify the pure data and find the mixed data. And then the mixed data are unmixed and classified by LSMA. The result of experiment shows that we can have the better performance in thematic information extraction with PHI by this means.

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Hong-gen Xu, Hong-chao Ma, De-ren Li, and Yan Song "Application of high spatial resolution airborne hyperspectral remote sensing data in thematic information extraction", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64190H (28 October 2006); https://doi.org/10.1117/12.712738

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KEYWORDS

Remote sensing

Neural networks

Roads

Reflectivity

Soil science

Image classification

Spatial resolution

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