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Proceedings Paper

Semi-supervised normalized embeddings for land-use classification from multiple view data
Author(s): Poppy G. Immel; Nathan D. Cahill
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Paper Abstract

Land-use classification from multiple data sources is an important problem in remote sensing. Data fusion algorithms like Semi-Supervised Manifold Alignment (SSMA) and Manifold Alignment with Schroedinger Eigenmaps (SEMA) use spectral and/or spatial features from multispectral, multimodal imagery to project each data source into a common latent space in which classification can be performed. However, in order for these algorithms to be well-posed, they require an expert user to either directly identify pairwise dissimilarities in the data or to identify class labels for a subset of points from which pairwise dissimilarities can be derived. In this paper, we propose a related data fusion technique, which we refer to as Semi-Supervised Normalized Embeddings (SSNE). SSNE is defined by modifying the SSMA/SEMA objective functions to incorporate an extra normalization term that enables a latent space to be well-defined even when no pairwise-dissimilarities are provided. Using publicly available data from the 2017 IEEE GRSS Data Fusion Contest, we show that SSNE enables similar land-use classification performance to SSMA/SEMA in scenarios where pairwise dissimilarities are available, but that unlike SSMA/SEMA, it also enables land-use classification in other scenarios.

Paper Details

Date Published: 8 May 2018
PDF: 13 pages
Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 1064408 (8 May 2018); doi: 10.1117/12.2304625
Show Author Affiliations
Poppy G. Immel, Rochester Institute of Technology (United States)
Ursa Space Systems (United States)
Nathan D. Cahill, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 10644:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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