This paper focuses on compressive sensing (CS)-based hyperspectral imaging (HSI). A band-by-band reconstruction approach, namely prior image constrained compressive sensing (PICCS)-based HSI, is proposed. Furthermore, a more effective PICCS model is built in this paper. Each hyperspectral band is reconstructed based on the previous one, which utilizes not only the sparsity of each hyperspectral band in a certain basis but also the similarity between two consecutive bands. Moreover, compared with the algorithms which reconstruct all the hyperspectral bands simultaneously, PICCS-based HSI reduces the requirements for computational ability and computational memory of the receivers. In addition, compared with the independent band-by-band reconstruction algorithms and tensor--based HSI, PICCS-based HSI significantly reduces the number of measurements with similar or better reconstruction quality. The convergence of the two algorithms is proved and some simulations are provided to illustrate their effectiveness.