Context-Aware Compressed Sensing of Hyperspectral Image

Abstract

Traditional hyperspectral imaging technique obtains numerous hyperspectral images (HSIs) with hundreds of spectral bands, leading to high cost in data acquisition, transmission, and storage. Compressed sensing (CS) theory provides a new imaging mechanism, which relies on the assumption that signals can be sparsely represented over a dictionary. By the CS imaging technique, original HSIs can be approximately reconstructed from only a few sampled measurements. In this article, a novel context-aware CS (CACS) method for HSIs is proposed by incorporating contextual prior to the dictionary learning and the sparse reconstruction. First, a patch-based online dictionary learning (ODL) algorithm is developed by introducing a joint sparse constraint. On the one hand, the online dictionary learning mechanism enables a more adaptive representation of HSIs with different scenes than using fixed-basis-based dictionaries, e.g., the discrete cosine transform (DCT) and the discrete wavelet transform dictionaries. On the other hand, the introduced joint sparse constraint promotes the learned dictionary to more sparsely and structurally represent spectral pixels. Then, with the well-learned dictionary, a weighted smoothing regularization is introduced to develop a new sparse reconstruction model. Considering the high spectral–spatial similarity of pixels in a neighborhood, the new sparse reconstruction model will encourage a locally smoothing reconstruction result. In this way, the spectral–spatial structures of the HSI can be well preserved, while possible artifacts can be effectively reduced. Experimental results demonstrate the superiority of the proposed method over some state-of-the-art hyperspectral compressive imaging methods.