Spatial-Spectral Joint Compressed Sensing for Hyperspectral Images

Abstract

Compressed sensing is one of the key technologies to reduce the volume of hyperspectral image for real-time storage and transmission. Reconstruction based on spectral unmixing show tremendous potential in hyperspectral compressed sensing compared with other conventional algorithms that directly reconstruct images. In this paper, a joint spatial-spectral joint compressed sensing scheme is proposed. In this scheme, compressed hyperspectral data are collected by spatial-spectral hybrid compressed sampling. As for the reconstruction, an objective function is developed by introducing the fidelity constraints of spatial and spectral measurements and the row sparsity constraint of abundance that guarantee the precise reconstruction of hyperspectral images and obtain endmembers and abundances as by-products accurately. An augmented Lagrangian type algorithm is meticulously elaborated to solve the above optimization problem. Extensive experimental results on several real hyperspectral datasets indicate that the proposed approach can achieve a reconstruction accuracy higher than those of other state-of-the-art methods. The efficiency and feasibility of the proposed scheme give it great potential in hyperspectral compressed sensing.

RGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy Images

“…Furthermore, LMM model was enhanced using seminonnegative matrix factorization for the reconstruction of the HSI which is performed by fusing spatial-spectral (SS) hybrid CS [15]. In the restoration process, fidelity restraints were proposed for an objective function in spectral and spatial estimations [16]. Consequently, the SS hybrid CS technique is proposed further for hyperspectral compressive sampling [17]- [19].…”

Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning

“…By dividing HSI into many blocks, Zhang et al [ 5 ] introduced an HSI data reconstruction method based on low-rank matrix recovery (LRMR). In particular, for this kind of signal, many new compressive sensing (CS)-based methods [ 9 , 10 , 11 ] have been proposed [ 6 , 8 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Golbabaee et al [ 6 ] simultaneously reconstructed HSI data with a low-rank and joint-sparse (L&S) structure by assuming that HSI data are low-rank and using a spatially joint-sparse wavelet representation.…”