Abstract
Saliency detection extracts objects attractive to a human vision system from an image. Although saliency detection methodologies were originally investigated on RGB color images, recent developments in imaging technologies have aroused the interest in saliency detection methodologies for data captured with high spectral resolution using multispectral and hyperspectral imaging (MSI/HSI) sensors. In this paper, we propose a saliency detection methodology that elaborates HSI data reconstructed through an autoencoder architecture. It resorts to (spectral-spatial) distance measures to quantify the salience degree in the data represented through the autoencoder. Finally, it performs a clustering stage in order to separate the salient information from the background. The effectiveness of the proposed methodology is evaluated with benchmark HSI and MSI data.
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Notes
- 1.
The main difference between multispectral and hyperspectral is the number of bands and how narrow the bands are. MSI technology commonly refers to a small amount of bands, i.e., from 3 to 10, sensed by a radiometer. HSI technology could have hundreds or thousands bands from a spectrometer. In this paper, we generally refer to HSI data defining a methodology that is then evaluated in both HSI and MSI scenario.
- 2.
The dataset is available at https://github.com/gistairc/HS-SOD.
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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 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. We thank Planetek Italia srl for Madrid data.
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Appice, A., Lomuscio, F., Falini, A., Tamborrino, C., Mazzia, F., Malerba, D. (2020). Saliency Detection in Hyperspectral Images Using Autoencoder-Based Data Reconstruction. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_15
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