**Dion Eustathios Olivier Tzamarias \*, Kevin Chow, Ian Blanes and Joan Serra-Sagristà**

Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, Spain; kevin.chow@uab.cat (K.C.); ian.blanes@uab.cat (I.B.); joan.serra@uab.cat (J.S.-S.)


Received: 30 August 2019; Accepted: 26 September 2019; Published: 30 September 2019

**Abstract:** Hyperspectral images are depictions of scenes represented across many bands of the electromagnetic spectrum. The large size of these images as well as their unique structure requires the need for specialized data compression algorithms. The redundancies found between consecutive spectral components and within components themselves favor algorithms that exploit their particular structure. One novel technique with applications to hyperspectral compression is the use of spectral graph filterbanks such as the GraphBior transform, that leads to competitive results. Such existing graph based filterbank transforms do not yield integer coefficients, making them appropriate only for lossy image compression schemes. We propose here two integer-to-integer transforms that are used in the biorthogonal graph filterbanks for the purpose of the lossless compression of hyperspectral scenes. Firstly, by applying a Triangular Elementary Rectangular Matrix decomposition on GraphBior filters and secondly by adding rounding operations to the spectral graph lifting filters. We examine the merit of our contribution by testing its performance as a spatial transform on a corpus of hyperspectral images; and share our findings through a report and analysis of our results.

**Keywords:** hyperspectral image coding; graph filterbanks; integer-to-integer transforms; graph signal processing
