Multiband and Lossless Compression of Hyperspectral Images

Pizzolante, Raffaele; Carpentieri, Bruno

doi:10.3390/a9010016

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…In this section, we focus on the simulation results achieved by the LBMHI algorithm, on Dataset 1 and on Dataset 2, which are comparable with the other state-of-the-art predictive-based approaches [11]. Moreover, it is important to note that the parameters of the LBMHI algorithm can be configured.…”

Section: Simulation Results Achieved By the Lbmhi Algorithmmentioning

confidence: 99%

“…The predictive-based multiband lossless compression for hyperspectral images (LMBHI) [11] algorithm exploits the inter-band correlation (i.e., the correlation among the neighboring pixels of contiguous bands) as well as the intra-band correlations (i.e., the correlations among the neighboring pixels of the same band), by using a predictive coding model.…”

Section: Multiband Lossless Compression Of Hyperspectral Imagesmentioning

confidence: 99%

“…Algorithms 2017, 10, 132 2 of 11 multiband lossless compression of hyperspectral images (LMBHI) algorithm [11] (reviewed in Section 2.1.2), which is a predictive-based lossless compression algorithm that achieves interesting results. Finally, we report and discuss the obtained experimental results in Section 3 and Section 4 draws our conclusions.…”

Section: An Efficient and Secure Transmission Frameworkmentioning

confidence: 99%

“…We highlight the key aspects of the proposed framework, in Section 2, and we outline further details for a possible effective implementation, in Section 2.1. Regarding the implementation, we considered a reversible invisible watermarking scheme (Section 2.1.1), designed specifically for hyperspectral images, and the multiband lossless compression of hyperspectral images (LMBHI) algorithm [11] (reviewed in Section 2.1.2), which is a predictive-based lossless compression algorithm that achieves interesting results.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Data: Efficient and Secure Transmission

Pizzolante

Carpentieri

2017

Algorithms

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Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications (e.g., mining and geology applications, ecology, surveillance, etc.). A hyperspectral image has a three-dimensional structure (a sort of datacube): it can be considered as a sequence of narrow and contiguous spectral channels (bands). The objective of this paper is to present a framework permits the efficient storage/transmission of an input hyperspectral image, and its protection. The proposed framework relies on a reversible invisible watermarking scheme and an efficient lossless compression algorithm. The reversible watermarking scheme is used in conjunction with digital signature techniques in order to permit the verification of the integrity of a hyperspectral image by the receiver.

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Section: Simulation Results Achieved By the Lbmhi Algorithmmentioning

confidence: 99%

Section: Multiband Lossless Compression Of Hyperspectral Imagesmentioning

confidence: 99%

Section: An Efficient and Secure Transmission Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Data: Efficient and Secure Transmission

Pizzolante

Carpentieri

2017

Algorithms

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“…At the decoder, as proposed in [35], the bands of HS images are divided into noisy bands and noiseless bands. Therefore, to properly exploit the inter-band correlation, for each band, we choose a number of the reconstructed bands belonging to the same class as its reference bands.…”

Section: A Framework Of the Proposed Schemementioning

confidence: 99%

Distributed Lossless Coding Techniques for Hyperspectral Images

Zhang

Chen

2015

IEEE J. Sel. Top. Signal Process.

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In this paper, we present a novel distributed coding scheme for lossless, progressive and low complexity compression of hyperspectral images. Hyperspectral images have several unique requirements that are vastly different from consumer images. Among them, lossless compression, progressive transmission, and low complexity onboard processing are three most prominent ones. To satisfy these requirements, we design a distributed coding scheme that shifts the complexity of data decorrelation to the decoder side to achieve lightweight onboard processing after image acquisition. At the encoder, the images are subsampled in order to facilitate successive encoding and progressive transmission. At the decoder, we generate the side information with adaptive region-based predictor by taking full advantage of the decoded subsampled images and previously decoded neighboring bands based on the assumptions that the objects appearing in different bands are highly correlated. The proposed progressive transmission via subsampling enables the spectral correlation to be refined successively, resulting in gradually improved decoding performance of higher-resolution layers as more sub-images are decoded. Experimental results on the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data demonstrate that the proposed scheme is able to achieve competitive compression performance comparing with the-state-of-the-art 3D schemes, including existing distributed source coding (DSC) schemes. The proposed scheme has even lower encoding complexity than that of the conventional 2D schemes.

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