Spectrum compression method based on KL (Karhunen-Loeve) transformation

Abstract

The invention discloses a spectrum compression method based on KL (Karhunen-Loeve) transformation, which comprises the following steps of: cutting and partitioning an original spectrum image; carrying out feature extraction on the spectrum image, which is partitioned into blocks, by using the KL transformation, and acquiring a compressed spectrum image according to number of eigen values required to be extracted. The spectrum compression method based on the KL transformation overcomes the problems of difficult real-time realization due to large computational burden and the like in the prior art and realizes the high-efficiency and high-fidelity compression of mass spectrum data.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, and in particular to a spectral compression method based on KL transformation. Background technique [0002] Remote sensing is a new comprehensive technology developed in the 1960s. It is closely related to science and technology such as space, electron optics, computer, and geography. It is one of the most powerful technical means for studying the earth's resources and environment. All objects have the characteristics of reflecting or radiating electromagnetic waves of different wavelengths. The technology of identifying objects and their existing environment by identifying electromagnetic waves is called remote sensing. With the development of remote sensing technology, spectral imaging technology has been more and more widely used. Spectral imaging technology integrates optics, spectroscopy, precision machinery, electronic technology and computer technology, and can ob...

AI Technical Summary

Problems solved by technology

However, since this transformation is related to the secondary statistics of the image, the extraction of the principal component image involves a huge amount of calculation and storage. The basis function of each transformation needs to calculate the covariance matrix, eigen Values ​​and eigenvectors, the amount of calculation is large, and the time consumption is large

In other words, the traditional KL transform can effectively compress spectral data, but it is difficult to apply it in real time because of its large amount of statistical calculations.

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