Abstract
In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results—the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain—than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance.
Highlights
Hyperspectral images contain both the spatial and spectral characteristics
In our previous research [21], we have developed single spectral compression sensing (SSCS) technology for plant hyperspectral data in the spectral domain
The spectral correlation of the adjacent bands is calculated in the spectral domain and the correlation of hyperspectral the adjacent bands is calculated in thedecided spectral domain andk-means the grouping of hyperspectral grouping of images is adaptively using the clustering algorithm
Summary
Hyperspectral images contain both the spatial and spectral characteristics. In recent years, they have been widely used in agriculture and forestry research, marine monitoring, natural disaster monitoring, and military reconnaissance [1]. With the increasing development of remote sensing technology, the requirement to increase the resolution of hyperspectral data has led to an extreme increase in its amount, which has caused tremendous pressure on the transmission and storage of hyperspectral images [2,3]. Solving this problem can start from the hardware itself, such as increasing the storage space of the hardware. Attempting to solve this problem from the hardware will inevitably raise the cost significantly, and turn the problem into an expensive hardware cost problem. Another feasible means to solve this problem is to perform effective data compression and solve the problem at the data source in the form of a small amount of information to represent all the information
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