Abstract
The huge volume of hyperspectral imagery demands enormous computational resources, storage memory, and bandwidth between the sensor and the ground stations. Compressed sensing theory has great potential to reduce the enormous cost of hyperspectral imagery by only collecting a few compressed measurements on the onboard imaging system. Inspired by distributed source coding, in this paper, a distributed compressed sensing framework of hyperspectral imagery is proposed. Similar to distributed compressed video sensing, spatial-spectral hyperspectral imagery is separated into key-band and compressed-sensing-band with different sampling rates during collecting data of proposed framework. However, unlike distributed compressed video sensing using side information for reconstruction, the widely used spectral unmixing method is employed for the recovery of hyperspectral imagery. First, endmembers are extracted from the compressed-sensing-band. Then, the endmembers of the key-band are predicted by interpolation method and abundance estimation is achieved by exploiting sparse penalty. Finally, the original hyperspectral imagery is recovered by linear mixing model. Extensive experimental results on multiple real hyperspectral datasets demonstrate that the proposed method can effectively recover the original data. The reconstruction peak signal-to-noise ratio of the proposed framework surpasses other state-of-the-art methods.
Highlights
Hyperspectral imagery (HSI) is different from conventional color images, and can collect tens or hundreds of spectrum samples for each image pixel
We compare the proposed distributed compressed hyperspectral sensing (DCHS) framework with several state-of-the-art reconstruction algorithms to evaluate the validity of the proposed framework, including MT-BCS [46], compressive-projection principal component analysis (CPPCA) [10], Spatio-spectral hybrid compressive sensing (SSHCS) [27], Spectral compressive acquisition (SpeCA) [15], and spectral compressed reconstruction based on spectral unmixing (SSCR_SU) [28]
The results further prove that hyperspectral compressed sensing reconstruction based on linear mixing model (LMM), such as DCHS, SSCR_SU, SpeCA, and SSHCS, is better than the reconstruction algorithms without using
Summary
Hyperspectral imagery (HSI) is different from conventional color images, and can collect tens or hundreds of spectrum samples for each image pixel. HSI is usually used as a three-dimensional (3D) data cube with 2D spatial and 1D spectral variation [1]. This kind of data potential is useful in applications in the food safety, biomedical, forensic, and industrial fields [2]. With the increase in spatial and spectral resolution, the amount of data of HSI increases dramatically. This has motivated the application of compressed sensing (CS) [3] techniques for hyperspectral imaging. HSI can be transformed into sparse signals by many popular sparsification techniques such as orthogonal transformation-based methods [4], dictionary-based methods [5], or spectral unmixing [6,7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
