Satellite Data Compression Research Articles

Decadal research and development of near lossless data compression on-board satellites at the Canadian Space Agency

This paper reviews the researches and developments in the last decade on near lossless satellite data compression techniques at the Canadian Space Agency (CSA). After briefly describing the two vector quantization based near lossless hyperspectral data compression techniques, it reviews the activities that assessed the near lossless properties of the compression techniques. The assessment results demonstrated that the compression errors introduced by the compression techniques are smaller than the intrinsic noise of the original data. This level of compression errors is considered as near lossless, as it has no impact or minor impact on the afterwards application utilization comparing the original data. This paper summarizes the activities of evaluating how satellite data product level impacts the compression performance for making decision whether or not on-board data processing is required or radiometric conversion should be applied on-board before compression. These evaluations examined the impact of the anomalies in raw hyperspectral data and the impact of on-board pre-processing and radiometric conversion on compression performance. The studies on the effect of spatial and spectral distortion of hyperspectral sensors on compression performance are also reviewed. This paper summarizes a multi-disciplinary user acceptability study that systematically assessed the impacts of the compression techniques on remote sensing products and applications. Eleven user groups covered a wide range of application areas and a variety of hyperspectral sensors participated in the study. This paper reviews the effort to explore the benefits of employing forward error correction to further enhance the resilience to bit-errors of the compressed data. The hardware developments are reported. Two versions of hardware compressor prototypes that implement the CSA near lossless compression techniques for on-board processing have been built.

Fractal compression of satellite images

Fractal geometry provides a means for describing and analysing the complexity of various features present in digital images. In this paper, characteristics of Fractal based compression of satellite data have been tested for Indian Remote Sensing (IRS) images (of different bands and resolution). The fidelity and efficiency of the algorithm and its relationship with spatial complexity of images is also evaluated. Results obtained from fractal compression have been compared with popularly used compression methods such as JPEG 2000, WinRar. The effect of bands and pixel resolution on the compression rate has also been examined. The results from this study show that the fractal based compression method provides higher compression rate while maintaining the information content of RS images to a great extent than that of JPEG. This paper also asserts that information loss due to fractal compression is minimal. It may be concluded that fractal technique has many potential advantages for compression of satellite images.

Optimal Compression of High Spectral Resolution Satellite Data via Adaptive Vector Quantization with Linear Prediction

Abstract Contemporary and future high spectral resolution sounders represent a significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. In this paper, a novel adaptive vector quantization (VQ)-based linear prediction (AVQLP) method for lossless compression of high spectral resolution sounder data is proposed. The AVQLP method optimally adjusts the quantization codebook sizes to yield the maximum compression on prediction residuals and side information. The method outperforms the state-of-the-art compression methods [Joint Photographic Experts Group (JPEG)-LS, JPEG2000 Parts 1 and 2, Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) 5/3, Context-Based Adaptive Lossless Image Coding (CALIC), and 3D Set Partitioning in Hierarchical Trees (SPIHT)] and achieves a new high in lossless compression for the standard test set of 10 NASA Atmospheric Infrared Sounder (AIRS) granules. It also compares favorably in terms of computational efficiency and compression gain to recently reported adaptive clustering methods for lossless compression of high spectral resolution data. Given its superior compression performance, the AVQLP method is well suited to ground operation of high spectral resolution satellite data compression for rebroadcast and archiving purposes.

Wavelet Compression and the Automatic Classification of Urban Environments Using High Resolution Multispectral Imagery and Laser Scanning Data

The field of wavelets has opened up new opportunities for the compression of satellite data. This paper examines the influence of data compression on the automatic classification of urban environments. Data from Daedalus airborne scanner imagery is used. Laser scanning altitude data is introduced as an additional channel alongside the spectral channels thus effectively integrating the local height and multispectral information sources. In order to incorporate context information, the feature base is expanded to include both spectral and non-spectral features. A maximum likelihood classification is then applied. It is demonstrated that the classification of urban scenes is considerably improved by fusing multispectral and geometric data sets. The fused imagery is then systematically compressed (channel by channel) at compression rates ranging from 5 to 100 using a wavelet-based compression algorithm. The compressed imagery is then classified using the approach described hereabove. Analysis of the results obtained indicates that a compression rate of up to 20 can conveniently be employed without adversely affecting the segmentation results.