TY - JOUR
T1 - Iterative enhanced multivariance products representation for effective compression of hyperspectral images
AU - Tuna, Süha
AU - Töreyin, Behçet Uğur
AU - Demiralp, Metin
AU - Ren, Jinchang
AU - Zhao, Huimin
AU - Marshall, Stephen
N1 - © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Effective compression of hyperspectral images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy hyperspectral image compression method based on Enhanced Multivariance Products Representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyperspectral images. In addition to these, an efficient variety of EMPR is also introduced in the paper, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak-signal-to-noise-ratio values and improved classification accuracy are achieved from EMPR based methods.
AB - Effective compression of hyperspectral images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy hyperspectral image compression method based on Enhanced Multivariance Products Representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyperspectral images. In addition to these, an efficient variety of EMPR is also introduced in the paper, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak-signal-to-noise-ratio values and improved classification accuracy are achieved from EMPR based methods.
KW - hyperspectral images
KW - classification accuracy
KW - enhanced multivariance products representation
KW - lossy compression
U2 - 10.1109/TGRS.2020.3031016
DO - 10.1109/TGRS.2020.3031016
M3 - Article
SN - 0196-2892
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
ER -