Space-spectrum joint sparse prior based satellitic hyperspectral compressed sensing reconstruction method

Abstract

The invention provides a space-spectrum joint sparse prior based satellitic hyperspectral compressed sensing reconstruction method. The method includes hyperspectral data-block diagonal random measurement of an onboard encoding end and compressed sensing construction of a ground decoding end. The onboard encoding-end adopts block-diagonal hyperspectral-data random measurement matrixes to perform independent random sampling on each spectral band so as to acquire measurement data and then sends the measurement data to the ground decoding end through a data link, the ground decoding end decomposes the data into low-rank components and sparse components, low-rand prior between the hyperspectral-data spectral bands and sparse prior in the spectral bands are jointed to establish a convex optimization reconstruction model, iteration solution is performed to obtain low-rank components and sparse components of reconstructed hyperspectral data, and the low-rank components and the sparse components are merged to obtain the reconstructed hyperspectral data. By the method, precision and efficiency of satellitic hyperspectral-image compressed sensing reconstruction are improved.

Description

technical field [0001] The invention belongs to the field of remote sensing image processing, and in particular relates to a satellite hyperspectral compressed sensing reconstruction method based on space-spectrum joint sparse prior. Background technique [0002] Hyperspectral remote sensing data has very high spectral resolution, and can obtain many very narrow spectral band information in the visible, near-infrared, mid-infrared and thermal infrared bands of the electromagnetic spectrum, thereby obtaining high-dimensional spectral data. However, the high spatial and spectral resolution also generates a large amount of measurement data, which brings difficulties to the storage, transmission and subsequent processing of hyperspectral remote sensing detection data. In particular, the continuous work of space-borne hyperspectral sensors will generate massive amounts of data, making it very difficult to store and transmit on satellite channels with limited bandwidth. Therefore...

AI Technical Summary

Problems solved by technology

[0004] The invention solves the problems of insufficient compression and low precision when compressing satellite hyperspectral remote sensing data in the prior art

Images