Abstract
Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth’s surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA), is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios.
Highlights
IntroductionThe algorithms for compressing hyperspectral images, as any other state-of-the-art compression algorithm, take advantage of the redundancies in the image samples to reduce the data volume.Hyperspectral image compression algorithms may take into consideration the redundancies in the spatial and spectral domains for reducing the amount of data with or without losing information.Lossless compression algorithms have been traditionally preferred to preserve all the information present in the hyperspectral cube for scientific purposes despite their limited compression ratio.the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use near-lossless and/or lossy compression techniques.The general approach for compressing hyperspectral images consists of a spatial and/or spectral decorrelator, a quantization stage and an entropy coder, which tries to use shorter codewords for representing the symbols
If the stopping condition is satisfied, the process finishes and no more pixels of the data set (Pixels) or V vectors are extracted, else, one new Pixel vector is extracted, its corresponding V vector is calculated and the stopping condition is checked again. This procedure enables a progressive decoding of the compressed bitstream
The standard describes that, in general, the Pairwise Orthogonal Transform (POT) provides better coding performance than the IWT, but requires more computational resources and has a more complex implementation. These assertions are empirically demonstrated in [5,7,41,42,43]. These results show that the compression performance of the Karhunen–Loève Transform (KLT) transform (PCA) clearly surpasses the compression performance provided by the rest of the transforms contained in the aforementioned standard
Summary
The algorithms for compressing hyperspectral images, as any other state-of-the-art compression algorithm, take advantage of the redundancies in the image samples to reduce the data volume.Hyperspectral image compression algorithms may take into consideration the redundancies in the spatial and spectral domains for reducing the amount of data with or without losing information.Lossless compression algorithms have been traditionally preferred to preserve all the information present in the hyperspectral cube for scientific purposes despite their limited compression ratio.the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use near-lossless and/or lossy compression techniques.The general approach for compressing hyperspectral images consists of a spatial and/or spectral decorrelator, a quantization stage and an entropy coder, which tries to use shorter codewords for representing the symbols. The algorithms for compressing hyperspectral images, as any other state-of-the-art compression algorithm, take advantage of the redundancies in the image samples to reduce the data volume. Hyperspectral image compression algorithms may take into consideration the redundancies in the spatial and spectral domains for reducing the amount of data with or without losing information. Lossless compression algorithms have been traditionally preferred to preserve all the information present in the hyperspectral cube for scientific purposes despite their limited compression ratio. The increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use near-lossless and/or lossy compression techniques. The general approach for compressing hyperspectral images consists of a spatial and/or spectral decorrelator, a quantization stage and an entropy coder, which tries to use shorter codewords for representing the symbols.
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