Rapid advancements in sensor technology have enabled satellites to generate a very large amount of data for a variety of applications, e.g., hyperspectral imagers and imaging Fourier transform spectrometers. These sensors acquire over hundreds to a few thousand spectral bands of images of a scene and generate a lot of data. As a result, there is an increasing need for data compression in order to effectively transmit data from space to ground and to archive data on the ground. Research into satellite data compression has been previously reported. This chapter describes lossless and near-lossless data compression techniques that have been applied to multispectral, hyperspectral, and ultraspectral satellite images, as well as the three international standards of satellite data compression. A more-comprehensive description of optical satellite data compression techniques and their implementation and applications can be found in this book’s companion text.
Data compression techniques can be classified into three types:
1. Lossless compression,
2. Near-lossless compression, and
3. Lossy compression.
Lossless compression techniques are reversible techniques that compress an image without loss of information. The reconstructed image is exactly the same as the original image. Because there is no loss of information, this kind of compression technique is used for applications that cannot tolerate any difference between the original and reconstructed data. However, a lossless compression technique cannot achieve a high compression ratio, which is dependent on the redundancy of the images. The larger the redundancy is, the higher the compression ratio that can be achieved. For optical satellite images, the lossless compression ratio is normally less than 3:1. For an image with a very smooth scene or extremely low spatial or spectral information in the data, a higher compression ratio may be achieved.
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