Abstract
When hyperspectral images are analyzed, a big amount of data, representing the reflectance at hundreds of wavelengths, needs to be processed. Hence, dimensionality reduction techniques are used to discard unnecessary information. In order to detect the so called “saliency”, i.e., the relevant pixels, we propose a bottom-up approach based on three main ingredients: sparse non negative matrix factorization (SNMF), spatial and spectral functions to measure the reconstruction error between the input image and the reconstructed one and a final clustering technique. We introduce novel error functions and show some useful mathematical properties. The method is validated on hyperspectral images and compared with state-of-the-art different approaches.
The research of Antonella Falini is founded by PON Project AIM 1852414 CUP H95G18000120006 ATT1. The research of Cristiano Tamborrino is funded by PON Project “Change Detection in Remote Sensing” CUP H94F18000270006. The research of the other authors is funded by PON Ricerca e Innovazione 2014–2020, the application of the proposed method to the saliency detection task is developed in partial fulfillment of the research objective of project RPASInAir “Sistemi Aeromobili a Pilotaggio Remoto nello spazio aereo non segregato per servizi” (ARS01 00820). While the application to the change detection task is developed in partial fulfillment of the research objective of the project “CLOSE to the Earth” (ARS01 00141).
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Acknowledgments
Antonella Falini, Cristiano Tamborrino and Francesca Mazzia are members of the INdAM Research group GNCS. Rosa Maria Mininni is member of the INdAM Research group GNAMPA. Annalisa Appice and Donato Malerba are members of the CINI Big Data Laboratory.
This work was also developed within the project “Modelli e metodi per l’analisi di dati complessi e voluminosi” of the University of Bari.
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Falini, A. et al. (2020). Novel Reconstruction Errors for Saliency Detection in Hyperspectral Images. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_12
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