Tensor completion for on-board compression of hyperspectral images
- 1 September 2010
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We present a new image compression scheme for hyperspectral images based on the newly-emerged matrix/tensor completion theory. Unlike typical transform-coding based methods, the proposed approach does not require any transform to be performed by the imaging sensor when doing on-board compression. Only a small set of pixels on a sparse set of locations on the imaging sensor needs to be captured and transmitted for each image. The decoder side relies on matrix/tensor completion for reconstructing the original images. Hence the scheme can drastically reduce the computation and bandwidth requirements on the on-board imaging sensors. Experiments show that the proposed method is able to obtain compression performance close to JPEG2000 while enjoying the afore-mentioned unique benefits.Keywords
This publication has 7 references indexed in Scilit:
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm MinimizationSIAM Review, 2010
- Hyperspectral image compression using distributed source coding and 3D SPECKPublished by SPIE-Intl Soc Optical Eng ,2009
- Segmented PCA and JPEG2000 for hyperspectral image compressionPublished by SPIE-Intl Soc Optical Eng ,2009
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse ProblemsSIAM Journal on Imaging Sciences, 2009
- Unified Lossy and Near-Lossless Hyperspectral Image Compression Based on JPEG 2000IEEE Geoscience and Remote Sensing Letters, 2008
- Three-Dimensional SPIHT Coding of Volume Images with Random Access and Resolution ScalabilityEURASIP Journal on Image and Video Processing, 2008
- JPEG2000 coding strategies for hyperspectral dataPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005