Self-modeling curve resolution can resolve the bilinear spectral datasets as the profiles of pure signal and their contributions which can be explained easily in physical and chemical meaning. With the advantage that their results can provide the pure spectra and the corresponding relative concentrations in the analyzed complex systems, MCR methods have been widely applied in the analysis of hyperspectral imaging. However, when the constraints applied are not strong enough, multivariate curve resolution models for bilinear data always suffer the problem of order ambiguity, scale ambiguity and rotation ambiguity which induce non-unique solution. Rotation ambiguity is the most difficult to be removed. To investigate the level of rotation ambiguity and provide the range of feasible solutions, in the published works, the researcher used grid search or Monte Carlo random sampling to display some feasible solutions fulfill the bilinear model under certain constraints. In this way, the concentrations and pure spectra results resolved by MCR can be better explained for their application by providing a range of feasible solutions. Polygons projected by feasible solutions based on geometry were also employed to illustrate the feasible solutions and the extension of rotation ambiguity. These methods normally are time consuming and cannot be used in the system with more than four components. More importantly, they cannot apply different constraints based on the properties of the analyzed samples, except non-negativity. In this work, we applied MCR-BANDS to evaluate the level of rotation ambiguity for resolved by MCR-ALS on the remote sensing hyperspectral imaging. In the first part, the mineral spectra selected from United States Geological Survey Committee were used for simulating a hyperspectral imaging dataset. In the simulated dataset, the noise level can be controlled and the differences of the spectral features between different components were easily identified. The concentrations of different components were simulated the real conditions, with gradual changes in the space. The rotation ambiguity was evaluated in the simulated data by using MCR-BANDS. To better explain the application of MCR-BANDS, this method was used to analyze a remote sensing dataset collected by Airborne Visible Infrared Imaging Spectrometer (AVIRIS), and the affection of rotation ambiguity on different components was visually displayed as concentration distributions in maps. The results show that MCR-BANDS can provide the level of feasible solutions of MCR-ALS by using maximum and minimum signal contribution functions (SCCF). This method can be applied to the system with any number of components, and can use almost all of the constraints which are chosen in MCR-ALS, like non-negativity, unimodality, closure, selectivity/local rank etc. The concentration distribution results from maximum and minimum SCCF are helpful to locate the specific targets in the remote sensing hyperspectral imaging.