Research on Compression Perceptual Hyperspectral Image Reconstruction Based on GISMT

Hyperspectral images contain rich information, including diversity of space, radiation and spectrum information. However, the huge amount of information also causes the problem of large amount of hyperspectral image data, which makes the hyperspectral image data face a lot of problems in the aspects of transmission and storage. To solve this problem, a compression sensing hyperspectral image reconstruction algorithm based on GISMT is proposed. This paper first introduces the improved joint sparse representation model, and then, based on the improved joint sparse representation model, designs a joint sparse representation solution algorithm based on GISA, GISMT, simulation results table. This method can improve the reconstruction effect of hyperspectral remote sensing image. Introduction In 2006, a new theoretical framework, compression perception (Compressive Sensing, CS) theory, was developed. In less than ten years, the Shannon-Nyquist sampling theorem was continuously perfected, and each of its research results could bring about revolutionary progress in the field of signal processing. Has the broad development prospect and the application space. Its core point is that assuming the signal can be represented by sparsity, as long as the signal is sparse or compressible, the low-dimensional observation signal can restore and reconstruct the high-dimensional original signal well, which brings a new direction to large-scale image sampling and processing. Although some achievements have been made in the study of the theory of compressed perception, there are still many problems that need to be studied and discussed in depth. Can we find a more sparse representation model to deal with the signal representation to meet the premise of sparse signal processing, so as to achieve a sufficient number of observations; Second, can we find an optimal orthogonal observation matrix for different reconstruction algorithms; third, find an optimal algorithm with low computational complexity to solve the NP difficult signal convex optimization problem, and quickly realize the reconstruction signal. Fourth, in the practical application of how to better The theory of compressed perception is combined with practical problems to solve practical problems. It has the following characteristics: First, the data amount is large, the information contained is much, but the data is redundant, and the correlation between the adjacent bands is high; Second, narrow band; the spectral interval between each band is generally less than or equal to l0nm; Third, high spectral resolution; There are only a few bands in IM, SPOT, and the width of bands is mostly 100-200nm. They all get images from several discrete bands. The hyperspectral band width is nanoscale, which can be used for continuous spectral imaging of the target. The influence of other interference factors is greatly restrained in spectral space. The absorption characteristic width of many ground objects is generally 20-40 nm, so hyperspectral remote sensing images can meet the general requirements of ground object detection and accurately detect non-detectable substances in wide band remote sensing, which is conducive to the fine recognition of ground objects. 233 Basic Description of 2 GISMT Algorithm Improved Joint Sparse Representation Model Suppose a N-dimensional signal xRN, then the transformation coefficient vector of x in Ψ domain can be expressed as Ψ Tx. If there are only K nonzero elements (N>>K) or only K large components in the transform coefficient Ψ, then the signal x is considered to be K sparse or approximate K sparse. Suppose there is a random projection M×N dimension (M < N) measurement matrix A, the linear projection of signal x results in the M dimension measurement value y, which is expressed as yaxx. The mathematical expression is as follows: (1) constrained optimization problem: || || . p x min x s t y Ax  ψ (1) Where p takes a value of 1 or 0. 1 || ||  that represents the sum of the absolute values of all elements in a vector; Represents 0 || ||  , representing the number of non-zero elements in a vector. Image priori knowledge plays a key role in the performance of image reconstruction algorithm. Remote sensing images themselves have two basic prior knowledge characteristics, namely, local smoothness and non-local self-similarity. The improved joint sparse representation model of remote sensing images is as follows:   cos L2D N3D || || λ || || + p p x x x        (2) In the formula, τ,λ denotes the regularization constraint term and tradeoff the sparsity of two prior knowledge. Pecifically, L2D || ||p x  Corresponding to the prior knowledge of local smoothness, the local continuity of image is maintained and the noise is effectively suppressed. The mathematical expression is as follows: L2D || || || || || || p v p h p p Dx D x D x x     (3) Where D is a gradient operator and Dh, Dv is a horizontal and vertical gradient operator. The global self-similarity of nonlocal 3D transform domain is described by combining the coefficient sparsity of all 3D groups of image blocks. The mathematical expression is as follows: 3 N3D 1 || || || || || ( ) || i n D x x i p p p x T Z       (4) A column vector representing all transform coefficients of an image x. Refer to the literature for a more detailed mathematical description. Consider the following constrained optimization problems: 2 2 1 || || || || 2 p x min y Ax x    (5) In the formula, λ nonnegative regularization parameter. Wangmeng Zuo et al are inspired by soft threshold and iterative contraction threshold method IST. A generalized iterative contraction algorithm (General Iterated Shrinkage Algorithm, is proposed. GISA) is used to solve a class of non-convex Lp norm sparse minimization problems. The general threshold function GST is as follows.           0 ; T ; sgn ; ; GST p GST p GST GST p p if y y y S y if y               (6)