Algorithms for striping noise compensation (SNC) for push-broom hyperspectral cameras (PBHCs) are primarily based on image processing techniques. These algorithms rely on the spatial and temporal information available at the readout data; however, they disregard the large amount of spectral information also available at the data. In this paper such flaw has been tackled and a multidimensional approach for SNC is proposed. The main assumption of the proposed approach is the short-term stationary behavior of the spatial, spectral, and temporal input information. This assumption is justified after analyzing the optoelectronic sampling mechanism carried out by PBHCs. Namely, when the wavelength-resolution of hyperspectral cameras is high enough with respect to the target application, the spectral information at neighboring photodetectors in adjacent spectral bands can be regarded as a stationary input. Moreover, when the temporal scanning of hyperspectral information is fast enough, consecutive temporal and spectral data samples can also be regarded as a stationary input at a single photodetector. The strength and applicability of the multidimensional approach presented here is illustrated by compensating for stripping noise real hyperspectral images. To this end, a laboratory prototype, based on a Photonfocus Hurricane hyperspectral camera, has been implemented to acquire data in the range of 400-1000 [nm], at a wavelength resolution of 1.04 [nm]. A mobile platform has been also constructed to simulate and synchronize the scanning procedure of the camera. Finally, an image-processing- based SNC algorithm has been extended yielding an approach that employs all the multidimensional information collected by the camera.