The information obtained by means of spectral remote sensing (i.e., the hyperspectral images) are involved in several real-life scenarios and applications. Historical research, monitoring of environmental hazards, forensics and counter-terrorism are some examples of contexts in which the hyperspectral data play an important role. In many contexts, the hyperspectral images could also play sensitive roles (e.g., in military applications, etc.) and are generally exchanged among several entities, in order to carry out different tasks on them. Therefore, it is important to guarantee their protection. A meaningful choice is the protection through data hiding techniques. In fact, by means of reversible data hiding techniques, the imaging data become a sort of information carrier and can be used for delivering other important data that can be used, for instance, to check the integrity of the original imaging data. In this paper, we introduce a one-pass framework that is able to perform the lossless data hiding and the lossless compression of the marked stream, at the same time, by exploiting the capabilities of the predictive paradigm. Substantially, in a single pass, a marked and compressed stego image is obtained, which can be exactly restored by the receiver: by decompressing and reversibly reconstructing the original unaltered image. In addition, our framework also permits to perform only the decompression (without the extraction of the hidden information). In this manner, the resulting stego (marked) hyperspectral image, could be used for several purposes, in which it is not necessary to extract the original data and an acceptable grade of degradation is tolerated. We also implement a proof-of-concept of the proposed framework to assess the effectiveness of our contribution. Finally, we report the achieved experimental results, which outperform other similar approaches.

One-pass lossless data hiding and compression of remote sensing data

Carpentieri, Bruno;Castiglione, Arcangelo;De Santis, Alfredo;Palmieri, Francesco;Pizzolante, Raffaele
2019

Abstract

The information obtained by means of spectral remote sensing (i.e., the hyperspectral images) are involved in several real-life scenarios and applications. Historical research, monitoring of environmental hazards, forensics and counter-terrorism are some examples of contexts in which the hyperspectral data play an important role. In many contexts, the hyperspectral images could also play sensitive roles (e.g., in military applications, etc.) and are generally exchanged among several entities, in order to carry out different tasks on them. Therefore, it is important to guarantee their protection. A meaningful choice is the protection through data hiding techniques. In fact, by means of reversible data hiding techniques, the imaging data become a sort of information carrier and can be used for delivering other important data that can be used, for instance, to check the integrity of the original imaging data. In this paper, we introduce a one-pass framework that is able to perform the lossless data hiding and the lossless compression of the marked stream, at the same time, by exploiting the capabilities of the predictive paradigm. Substantially, in a single pass, a marked and compressed stego image is obtained, which can be exactly restored by the receiver: by decompressing and reversibly reconstructing the original unaltered image. In addition, our framework also permits to perform only the decompression (without the extraction of the hidden information). In this manner, the resulting stego (marked) hyperspectral image, could be used for several purposes, in which it is not necessary to extract the original data and an acceptable grade of degradation is tolerated. We also implement a proof-of-concept of the proposed framework to assess the effectiveness of our contribution. Finally, we report the achieved experimental results, which outperform other similar approaches.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4714794
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