@inproceedings{eeab67184ade441d97693dd10438811d,
title = "An optimal-truncation-based tucker decomposition method for hyperspectral image compression",
abstract = "Hyperspectral images (HSI) contain hundreds of bands, which brings huge amount of data. In this paper, a novel compression method based on optimal-truncation tucker decomposition for HSI is proposed. HSI tensor is firstly decomposed into complete core tensor. And then core tensor and factor matrices are truncated according to the optimal number of components of core tensor along each mode (NCCTEM), which is determined by the proposed criterion for the optimal NCCTEM and searching strategy. Experimental results show that the proposed method has the excellent reconstruction comparable to the traditional compression methods. Furthermore, it significantly reduces the compression and decompression time.",
keywords = "Hyperspectral images, Image Compression, Optimal truncation, Tucker Decomposition",
author = "Hao Chen and Wei Lei and Shuang Zhou and Ye Zhang",
year = "2012",
doi = "10.1109/IGARSS.2012.6350833",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4090--4093",
booktitle = "IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium",
address = "美国",
note = "32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 ; Conference date: 22-07-2012 Through 27-07-2012",
}