Earticle

초록

영어

According to the characteristics of hyperspectral images, a novel compression and reconstruction algorithm for hyperspectal images based on compressed sensing is proposed. The random measurements of each image and the linear prediction coefficients are made at the encoder, and then transmitted sequentially to the decoder. At the decoder, in terms of apparent correlations between the adjacent spectral bands, a de-correlation algorithm based on block linear prediction model is used in reconstruction process. The inter-band redundancies are removed from the measurements of current image, thus the de-correlation image data is sparser, which can be reconstructed easily. Experimental results show that the proposed algorithm achieves improved reconstruction performance and efficiently reduces the cost of computation at the encoder, which is more suitable for hardware implementation.

목차

Abstract
 1. Introduction
 2. Proposed Scheme
  2.1. CS Basic Principles
  2.2. Hyperspectral Image Compressed Sensing
  2.3. Block Linear Prediction Model
  2.4. Block Linear Prediction GPSR
 3. Simulations
 4. Conclusion
 References

키워드

저자

• Xu Cheng [ College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China ]
• Huang Daqing [ Research Institute of UAV, Nanjing University of Aeronautics and Astronautics, Nanjing, China ]
• Han Wei [ College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605