Paper Info
Title
Tensor Dictionary Learning with Representation Quantization for Remote Sensing Observation Compression
Abstract
Nowadays, multidimensional data structures, known as tensors, are widely used in many applications like earth observation from remote sensing image sequences. However, the increasing spatial, spectral and temporal resolution of the acquired images, introduces considerable challenges in terms of data storage and transfer, making critical the necessity of an efficient compression system for high dimensional data. In this paper, we propose a tensor-based compression algorithm that retains the structure of the data and achieves a high compression ratio. Specifically, our method learns a dictionary of specially structured tensors using the Alternating Direction Method of Multipliers, as well as a symbol encoding dictionary. During run-time, a quantized and encoded sparse vector of coefficients is transmitted, instead of the whole multidimensional signal. Experimental results on real satellite image sequences demonstrate the efficacy of our method compared to a state-of-the-art compression method.
Year
DOI
Venue
2020
10.1109/DCC47342.2020.00036
2020 Data Compression Conference (DCC)
Keywords
DocType
ISSN
high dimensional data,tensor-based compression algorithm,high compression ratio,specially structured tensors,alternating direction method of multipliers,symbol encoding dictionary,multidimensional signal,satellite image sequences,state-of-the-art compression method,tensor dictionary learning,representation quantization,remote sensing observation compression,multidimensional data structures,earth observation,remote sensing image sequences,increasing spatial resolution,spectral resolution,temporal resolution,data storage,efficient compression system
Conference
1068-0314
ISBN
Citations 
PageRank 
978-1-7281-6458-8
0
0.34
References 
Authors
13
3
Name
Order
Citations
PageRank
Anastasia Aidini122.09
Grigorios Tsagkatakis212221.53
P. Tsakalides3954120.69