Embedded lossy to lossless compression of hyperspectral images using JPEG 2000

IEEE Trans. Geosci. Remote Sensing

et al. 2007

267

153

Abstract-Transform-based lossy compression has a huge potential for hyperspectral data reduction. Hyperspectral data are 3-D, and the nature of their correlation is different in each dimension. This calls for a careful design of the 3-D transform to be used for compression. In this paper, we investigate the transform design and rate allocation stage for lossy compression of hyperspectral data. First, we select a set of 3-D transforms, obtained by combining in various ways wavelets, wavelet packets, the discrete cosine transform, and the Karhunen-Loève transform (KLT), and evaluate the coding efficiency of these combinations. Second, we propose a low-complexity version of the KLT, in which complexity and performance can be balanced in a scalable way, allowing one to design the transform that better matches a specific application. Third, we integrate this, as well as other existing transforms, in the framework of Part 2 of the Joint Photographic Experts Group (JPEG) 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. We introduce an evaluation framework based on both reconstruction fidelity and impact on image exploitation, and evaluate the proposed algorithm by applying this framework to AVIRIS scenes. It is shown that the scheme based on the proposed low-complexity KLT significantly outperforms previous schemes as to rate-distortion performance. As for impact on exploitation, we consider multiclass hard classification, spectral unmixing, binary classification, and anomaly detection as benchmark applications.

mentioning

confidence: 99%

Transform Coding Techniques for Lossy Hyperspectral Data Compression

Penna

Tillo

IEEE Trans. Geosci. Remote Sensing

et al. 2007

267

153

“…For lossy compression we have employed two algorithms based on the multicomponent transformation extension of JPEG 2000 Part 2. The first algorithm uses the KLT as spectral decorrelator, followed by JPEG 2000 spatial decorrelation and coding, including 3D post-compression rate-distortion optimization [9]. The second algorithm is similar to the first one, but employs a spectral discrete wavelet transform (DWT) [8]; all spectral decomposition levels are carried out before performing the spatial wavelet transform.…”

Section: Resultsmentioning

confidence: 99%

Quality Assessment for Hyperspectral Imagery: Comparison Between Lossy and Near-Lossless Compression

Penna

Tillo

2006 Fortieth Asilomar Conference on Signals, Systems and Computers

et al. 2006

In the field of remote sensing image compression it is often argued that traditional MSE-based fidelity metrics might not effectively describe the quality of remote sensing lossy or near-lossless compressed images. In this paper we introduce a performance evaluation framework based on both reconstruction fidelity and impact on image exploitation. Besides MSE, the framework also considers hard classification and mixed pixel classification, as well as anomaly detection. We apply this framework to evaluate and compare the quality of state-of-the-art lossy and near-lossless compression techniques applied to hyperspectral AVIRIS scenes.

“…Having said that, the transform that typically provides the best results is the spectral KLT. This transform has been employed as a spectral decorrelator by many authors [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], and is known to be optimal for decorrelation of Gaussian processes [43]. KLT has a non-negligible computational cost and several approaches have been proposed to alleviate this complexity [44], [45].…”

Section: Multi-component Compressionmentioning

confidence: 99%

A Tutorial on Image Compression for Optical Space Imaging Systems

Blanes

IEEE Geosci. Remote Sens. Mag.

Serra-Sagristà

2014

Public policies and private initiatives share the will to explore outer space and to monitor the Earth from space sensors. Recent years have seen an increased number of space missions, while the sensors on board aircrafts or spacecrafts have also significantly improved their acquisition capabilities. Given this huge volume of remote sensing data and the detailed characteristics of the acquired images, a data compression process is in order to allow as large a transmission rate as possible.In this paper we provide an overview of several standards for remote sensing data compression, notably of those recently approved by the Consultative Committee for Space Data Systems, although the use of other ISO/IEC image coding standards is also dealt with. Discussion embraces both mono band and multi band compression, and lossless, lossy and near-lossless compression.Illustrative results are reported for a set of AVIRIS and Hyperion images, indicating that exploiting the spectral correlation -either in prediction-based or in transform-based schemes-is paramount to achieve improved coding performance.Index Terms-image compression, multispectral and hyperspectral images, CCSDS, prediction, transform coding, DPCM, rate control.Post-print of: "A tutorial on image compression for optical space imaging systems" / I.