24 May 2012 In-track multi-angle model portability of multispectral land-cover classification using very high spatial resolution data
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Abstract
In this paper, we present a method to analyze the impact of data space normalization on spectral classification model portability using multi-angle very-high spatial resolution imagery. In-track multi-angle data provide images of a single scene, from different observation angles, during a very short period of time. This creates a sequence of images with relatively static atmospheric and illumination conditions. With this data, the only changes in the scene are due to observation angle and surface reflectance properties. Using this information, we present an analysis of both the impact of surface anisotropy and data space normalization on spectral classification accuracy and model portability.
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Nathan Longbotham, Fabio Pacifici, William Emery, "In-track multi-angle model portability of multispectral land-cover classification using very high spatial resolution data", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901V (24 May 2012); doi: 10.1117/12.919581; https://doi.org/10.1117/12.919581
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