This paper presents a novel panoramic hyperspectral stereo imaging system with depth map estimation capability. Images are collected from two line scan hyperspectral cameras in stereo configuration, while the platform is rotating. The proposed system combines the strengths of hyperspectral, stereo, and panoramic imaging. In addition, the proposed system uses passive sensors and hence does not need any radiation source, unlike LiDAR-based systems. In this paper, the proposed system, which captures hyperspectral data from 400 to 1000 nm wavelengths, is introduced, the stereo calibration procedure is described, corresponding analytic analyses are carried out with relation to rotating line-scan camera theory, and critical system parameters and aspects are addressed. In addition, a novel multiband stereo matching approach, which introduces multiband Census transform and SAD-based cost aggregation to stereo matching literature, is proposed. Experimental results are evaluated using two hyperspectral datasets, and the performance of the proposed algorithm is compared with the performance of several prominent matching algorithms available in the literature. A LiDAR-based system is used to enable an analysis of depth information accuracy. The experimental results show the enhanced matching and depth estimation performance of the proposed matching approach over standard stereo methods, and the consistency of depth values between LiDAR and the proposed system. The proposed system, which combines the strengths of panoramic imaging and stereo imaging with the high spectral resolution of hyperspectral cameras, uses passive imaging solutions and is fit to be used in applications requiring depth computation, target detection, and change detection.