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Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification

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Abstract

Hyperspectral (HS) images captured from Earth by satellite and aircraft have become increasingly important in several environmental and ecological contexts (e.g. agriculture and urban areas). In the present study we propose an iterative learning methodology for the change detection of HS scenes taken at different times in the same areas. It cascades clustering and classification through iterative learning, in order to separate salient regions, where a change occurs in the scene from the unchanged background. The iterative learning is evaluated in both the clustering and the classification steps. The experiments performed with the proposed methodology provide encouraging results, also compared to several recent state-of-the-art competitors.

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Notes

  1. In principle, any other pixel-wise distance measure can be implemented for this analysis.

  2. As clarified in Section 3.3.1, clustering is performed on pixels spanned on the feature D—distance computed pixel-wise between the spectral vectors of the bi-temporal scene, according to the theory reported in Section 3.2—and Y — saliency label pixel-wise decided at the completion of the previous iteration. In the first iteration clustering is performed on D (as Y will be computed only after the completion of the classification step), while in each subsequent iteration clustering is performed on D × Y. However, as the information on D is available from the first iteration, we are able to compute (8) at each iteration.

  3. The auto-encoding technique may also be used to reduce the dimensionality of the fused spectral data, even if a recent research shows that no significant improvement can be actually achieved in HS imagery analysis by considering auto-encoding instead of principal components [6].

  4. Correspondence and requests for materials should be addressed to Annalisa Appice (email: annalisa.appice@uniba.it).

  5. https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html

  6. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html

  7. https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

  8. https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset

  9. https://rslab.ut.ac.ir/documents/81960329/82017906/Hyperspectral_ChangeDetection_Code.zip

  10. https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset

  11. https://gitlab.citius.usc.es/hiperespectral/ChangeDetectionDataset

  12. We use the Matlab implementation of US-ELM available on https://www.ntu.edu.sg/home/egbhuang/elm_codes.html. We run US-ELM with the dimension of the embedding layer varying between 2, 3, 4 and 5. We consider the results achieved from the trial with the highest accuracy.

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Acknowledgments

This work fulfills the research objectives of the PON ”Ricerca e Innovazione” 2014–2020 project RPASinAir ”Integrazione dei Sistemi Aeromobili a Pilotaggio Remoto nello spazio aereo non segregato per servizi” (ARS01_00820), funded by the Italian Ministry for Universities and Research (MIUR). The authors thank Lynn Rudd for her help in reading the manuscript, López Fandiño Javier for providing bi-temporal imagery data of Hermiston1, Santa Barbara and Patterson, Mahdi Hasanlou for providing bi-temporal imagery data of Hermiston2 and Francesco Lomuscio for running experiments with AICA.

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Authors and Affiliations

  1. Department of Informatics, Università degli Studi di Bari Aldo Moro, via Orabona, 4, I-70125, Bari, Italy

    Annalisa Appice, Emilio Acciaro & Donato Malerba

  2. Consorzio Interuniversitario Nazionale per l’Informatica - CINI, Bari, Italy

    Annalisa Appice & Donato Malerba

  3. Dipartimento di Ingegneria Elettrica ed Informazione, Politecnico di Bari, via Orabona, 4, I-70125, Bari, Italy

    Pietro Guccione

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  1. Annalisa Appice
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Appice, A., Guccione, P., Acciaro, E. et al. Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification. Appl Intell 50, 3179–3200 (2020). https://doi.org/10.1007/s10489-020-01701-8

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