3D Modified No List Set Partitioning in Hierarchical Trees Coding for Hyperspectral Image Sensors

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 3

Year of Publication : 2026

Authors : Purushottam Lal Nagar, Shrish Bajpai

10.14445/23488549/IJECE-V13I3P102

How to Cite?

Purushottam Lal Nagar, Shrish Bajpai, "3D Modified No List Set Partitioning in Hierarchical Trees Coding for Hyperspectral Image Sensors," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 3, pp. 13-27, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I3P102

Abstract:

Hyperspectral images have rich spatial-spectral information, which is used in various applications. But the large size of hyperspectral images poses a significant challenge to the image sensor performance. To properly handle the HS image data, an efficient compression algorithm is required to compress the HS image data. There are many types of compression algorithms that have been proposed in the past, but wavelet transform-based set-partitioned hyperspectral compression algorithms have superior coding performance. But, these compression algorithms do not provide the desired features of progressive transmission and spatial scalability at very low bit rate coding. Also, these compression algorithms use a linked list for tracking of the coefficients during the coding process, which becomes a bottleneck for fast implementation. The proposed compression algorithm uses markers instead of lists, which reduces the complexity significantly by ~10% to ~20% with reference to 3D-NLS. The significance reordering of the transform coefficients in the proposed algorithm ensures that highly significant coefficients are encoded first to exploit the energy compaction of the transform and increase the coding efficiency. Thus, the proposed compression algorithm is a choice for the low-resource HS image sensors.

Keywords:

Hyperspectral Image, Compression, Zerotree, Set Partition Hyperspectral Image Compression Algorithm, Coding, Wireless Sensor Network, Energy Efficiency.

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