3D Shearlet Transform-based Block Cube Tree Coding for Resource Constraint Hyperspectral Image Sensors

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 1

Year of Publication : 2026

Authors : Purushottam Lal Nagar, Shrish Bajpai

10.14445/23488549/IJECE-V13I1P115

How to Cite?

Purushottam Lal Nagar, Shrish Bajpai, "3D Shearlet Transform-based Block Cube Tree Coding for Resource Constraint Hyperspectral Image Sensors," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 1, pp. 174-192, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I1P115

Abstract:

Compression algorithms are absolutely necessary for the effective storage and speedy transfer of remote imaging data. The present manuscript proposes a transform-based hyperspectral image compression algorithm that exploits both the inter- and intra-subband correlations among the transform coefficients. The compression algorithm is based on the Spatial Oriented Trees (SOTs), which are the basic unit in block cubes. In contrast to the hierarchical tree compression approach, which only uses a single coefficient for 3D set partitioning, the block cube data structure takes the form of a cube and has the coefficients m*m*m. The root node of each SOT is located in the LLL band, while child and descendant blocks are located in the high-frequency sub-band. The proposed compression algorithm exploits the best features of the zeroblock cube and zerotree compression algorithms.

Keywords:

Transform Coding, Hyperspectral Image Compression, Shearlet transform, Transform coefficients, Coding efficiency.

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