Two Dimensional Compressed Sampling Reconstruction of Hyperspectral Images based on Spectral Prediction

Li Wang ； Wei Wang ； Boni Liu

《International Core Journal of Engineering》 6卷5期 (2020/05) Pp. 125-132

https://doi.org/10.6919/ICJE.202005_6(5).0020

摘要

The development of compressive techniques of hyperspectral images (HSI) becomes critical due to the limitations of the requirements of data storage, transmission and processing. Recently, Compressed Sampling (CS) has been used for hyperspectral imaging for its ability to recover the original data exactly under certain condition at a much lower sampling rate than Nyquist rate. In this paper, a residual reconstruction algorithm incorporating with two dimensional compressed sampling (2DCS) for Hyperspectral images is proposed to improve the performance of reconstruct algorithm. In the reconstruction process, spectral prediction is introduced for the strong spectral correlation between hyperspectral image bands. The experimental results reveal that the proposed technique achieves significantly higher quality than a straightforward reconstruction that reconstructs the hyperspectral images band by band independently. Meanwhile, the comparison between 2DCS and block-based compressed sampling (BCS) is developed and the results demonstrate that the superiority of 2DCS over BCS is in terms of high PSNR with respect to sampling rate.

關鍵字

Hyperspectral images ； Two dimensional compressed sampling ； Spectral prediction ； PSNR

參考文獻

NIU Y and WANG B. Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1604-1617. DOI: 10.1109/TGRS.2016.2628085.

REN Z， WU L. Spectral-spatial Classification for Hyperspectral Imagery Based on Intrinsic Image Decomposition [J]. Spacecraft Recovery & Remote Sensing, 2019, 40(3): 111-120. DOI:10.3969/j.issn.1009-8518.2019.03.014.

CHEN S, ZHOU Y, QI R. Joint sparse representation of hyperspectral image classification based on kernel function [J]. Systems Engineering and Electronics, 2018, 40(3): 692-698. DOI: 10.3969 /j.issn. 1001- 506X. 2018. 03. 31. (in Chinese)

FANG L, HE N, LI S, et al. A New Spatial-Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation [J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6): 3534-3546. DOI: 10.1109/ TGRS.2018.2801387.

PAN L, LI H C, MENG H, et al. Hyperspectral Image Classification via Low-Rank and Sparse Representation with Spectral Consistency Constraint [J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(11): 2117-2121.