Abstract
Hyperspectral image (HSI) compression has recently become a popular research area in remote sensing applications. However, existing clustering and compression techniques are not based on the intensity value and work by forming static rules. To overcome these issues, a novel lossy (nearly lossless) compression algorithm based on the distributed visual assessment cluster tendency (DVAT) – singular value decomposition (SVD) technique is proposed. At first, the given HSI image is preprocessed by cellular automata filtering to remove the white Gaussian and speckle noise. After that, the preprocessed image is split into bands, which are formed to a distance matrix. Here, the distances between the image bands are predicted to identify pixels with related intensity values. Hence, the DVAT technique is implemented to cluster the nearest distance images. Then, the label of the cluster is optimally selected for segregating the band into a band index. Consequently, the cluster index is formed and the image matrix is clustered based on the label features. After that, the SVD technique is applied to encode the HSI band image. Therefore, the discrete wavelet transform technique is applied for transformation and the transformed cell redundancy check technique is employed to encode the image band. Then, the image is reconstructed by reversing the above processes. The major advantages of the proposed method comprise a cleared clustered output, a correct index value, and the reduction of data losses during compression. During experimental results, the performance of the proposed method was evaluated in terms of the compression ratio, the mean-square error, and the peak signal-to-noise ratio.
Zusammenfassung
Ein verbesserter Algorithmus zur Kompression hyperspektraler Bilddaten basierend auf DVAT-SVD. Die Kompression hyperspektraler Bilddaten (HSI) hat in jüngerer Vergangenheit zunehmend an Bedeutung für fernerkundliche Anwendungen gewonnen. Existierende Cluster- und Kompressionstechniken basieren allerdings nicht auf dem Intensitätswert, sondern lediglich auf statischen Regelwerken. In diesem Beitrag wird eine neue, verlustarme (quasi verlustfreie) Datenkompressionsmethode vorgestellt, die auf der Distributed Visual Assessment Cluster Tendency (DVAT) – Singulärwertzerlegung (SVD) basiert. Zunächst werden aus dem vorliegenden HSI mittels Zellularer Automaten das weiße Gaußsche Rauschen sowie der Speckle herausgefiltert. Für die Kompression wird aus den einzelnen vorverarbeiteten Bändern eine Distanzmatrix aufgebaut. Dazu werden die Abstände zwischen den einzelnen Bändern berechnet, die wiederum als Eingang für eine Clusteranalyse mittels der DVAT-Technik verwendet werden. Die anschließende SVD zur eigentlichen Kompression der HSI beruht auf der diskreten Wavelet Transformation (DWT). Zur Verschlüsselung kommt der Transformed Cell Redundancy Check (TCRC) zum Einsatz. Das Bild kann durch die entsprechenden Umkehrprozesse rekonstruiert werden. Wesentliche Vorteile des vorgestellten Ansatzes liegen in einem bereinigten Cluster-Output, einem korrekten Indexwert sowie einem niedrigen Datenverlust durch die Kompression. Die Performance der neuen Methodik wurde mittels der Kompressionsratio (CR), dem mittleren quadratischen Fehler (MSE) und dem Peak des Signal-Rausch-Verhältnisses (PSNR) evaluiert.
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02 October 2017
An erratum to this article has been published.
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The original version of this article was revised. The authors first and family names were exchanged.
An erratum to this article is available at https://doi.org/10.1007/s41064-017-0026-z.
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Thiyagarajan, S., Gnanadurai, D. An Improved Compression Algorithm for Hyperspectral Images based on DVAT-SVD. PFG 85, 169–181 (2017). https://doi.org/10.1007/s41064-017-0017-0
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DOI: https://doi.org/10.1007/s41064-017-0017-0