Efficient Compression of Hyperspectral Images Using Optimal Compression Cube and Image Plane

Research output: Contribution to journalArticle

Abstract

Hyperspectral (HS) images (HSI) provide a vast amount of spatial and spectral information based on the high dimensionality of the pixels in a wide range of wavelengths. A HS image usually requires massive storage capacity, which demands high compression rates to save space with preservation of data integrity. HS image can be deemed as three dimensional data cube where different wavelengths (W) form the third dimension along with X and Y dimensions. To get a better compression result, spatial redundancy of HS images can be exploited using different coders along X, Y, or W direction. This article focuses on taking maximum advantage of HS images redundancy by rearranging HS image into different 3D data cubes and proposes a directionlet based compression scheme constituted the optimal compression plane (OCP) for adaptive best approximation of geometric matrix. The OCP, calculated by the spectral correlation, is used to the prediction and determination of which reconstructed plane can reach higher compression rates while minimizing data loss of hyperspectral data. Moreover, we also rearrange the 3D data cube into different 2D image planes and investigate the compression ratio using different coders. The schema can be used for both lossless and lossy compression. Our experimental results show that the new framework optimizes the performance of the compression using a number of coding methods (inclusive of lossless/lossy HEVC, motion JPEG, JPG2K, and JPEG) for HSIs with different visual content.
Original languageEnglish
Pages (from-to)167-179
Number of pages13
JournalLecture Notes in Computer Science
Volume8935
DOIs
Publication statusPublished - 2015

Fingerprint Dive into the research topics of 'Efficient Compression of Hyperspectral Images Using Optimal Compression Cube and Image Plane'. Together they form a unique fingerprint.

Cite this