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A Prediction-Based Spatial-Spectral Adaptive Hyperspectral Compressive Sensing Algorithm.

Ping Xu1, Bingqiang Chen2, Lingyun Xue3

  • 1College of Life Information & Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China. xuping@hdu.edu.cn.

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|October 3, 2018
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Summary
This summary is machine-generated.

A new prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm improves hyperspectral data storage and transmission. PSSAHCS achieves superior reconstruction performance and denoising capabilities compared to existing methods.

Keywords:
compressive sensinghyperspectral imagesinterspectral predictionspatial-spectral adaptation

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Area of Science:

  • Remote Sensing
  • Signal Processing
  • Data Compression

Background:

  • Massive hyperspectral data presents significant storage and transmission challenges.
  • Existing hyperspectral compressive sensing algorithms have limitations in performance and reconstruction accuracy.

Purpose of the Study:

  • To propose a novel prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm.
  • To enhance the efficiency and accuracy of hyperspectral data compression and reconstruction.

Main Methods:

  • Adaptive spatial block size determination based on spatial self-correlation.
  • K-means clustering for hyperspectral image grouping.
  • Local Means and Local Standard Deviations (LMLSD) for key band selection and linear prediction smoothing.
  • Random Gaussian measurement matrix and Discrete Cosine Transform (DCT) sparse basis.
  • Stagewise Orthogonal Matching Pursuit (StOMP) for reconstruction.

Main Results:

  • PSSAHCS demonstrated superior performance in subjective evaluation, peak signal-to-noise ratio, and spatial autocorrelation.
  • Improved spectral curve comparison and inter-spectra correlation in the spectral domain.
  • Effective compression and reconstruction of hyperspectral images with strong denoising capabilities.

Conclusions:

  • The proposed PSSAHCS algorithm offers significant improvements over SSCS, BHCS, and AGDCS.
  • PSSAHCS provides an effective solution for efficient storage and transmission of hyperspectral data.
  • The algorithm exhibits robust performance in both spatial and spectral domains, along with effective noise reduction.