Improved Grouping and Noise Cancellation for Automatic Lossy Compression of AVIRIS Images - Advanced Concepts for Intelligent Vision Systems - page 260

Improved Grouping and Noise Cancellation for
Automatic Lossy Compression of AVIRIS Images

Nikolay Ponomarenko 1 , Vladimir Lukin 1 , Mikhail Zriakhov 1 , and Arto Kaarna 2

1 National Aerospace University
Department of Transmitters, Receivers and Signal Processing
17 Chkalova Street, 61070 Kharkov, Ukraine
2 Lappeenranta University of Technology
Department of Information Technology
Machine Vision and Pattern Recognition Laboratory
P.O. Box 20, FI-53851 Lappeenranta, Finland
[e-mail], [e-mail], [e-mail]

Abstract. An improved method for the lossy compression of the AVIRIS
hyperspectral images is proposed. It is automatic and presumes blind
estimation of the noise standard deviation in component images, their
scaling (normalization) and grouping. A 3D DCT based coder is then
applied to each group to carry out both the spectral and the spatial
decorrelation of the data. To minimize distortions and provide a su-
cient compression ratio, the quantization step is to be set at about 4.5.
This allows removing the noise present in the original images practically
without deterioration of the useful information. It is shown that for real
life images the attained compression ratios can be of the order 8 . . . 35.

Keywords: remote sensing, hyperspectral images, noise estimation, noise
cancellation, image compression, decorrelation.

1 Introduction

Hyperspectral imaging has gained wide popularity in recent two decades [1] [2].
Remote sensing (RS) hyperspectral images (HSI) as those ones formed by the
AVIRIS, HYPERION, CHRIS-PROBA and other sensors are characterized by
large amount of data [1]-[3]. Thus, their compression for transferring, storage,
and offering to users is desirable.
Even the best lossless coders provide a compression ratio (CR) not larger
than 4 for such data [3], [4], and this is often not appropriate. Therefore, the
application of the lossy compression becomes necessary [3], [5]-[8]. There are
many methods of HSI lossy compression already developed. To be ecient, these
methods have to exploit both the sucient spectral (inter-channel) and spatial
correlation of the data inherent for HSI [2]. This is usually done by carrying
out the spectral decorrelation first which is followed by reducing the spatial
redundancy.