Because of the influence of certain operational parameters, such as sensor status, imaging mechanism, climate, and illumination, hyperspectral remote sensing images suffers from serious distortion. Intrinsic image decomposition (IID) is an extensively used image processing technology in the field of computer vision and graphics because it can acquire the essential features of the images that are being processed. IID is introduced to hyperspectral image procesing to decoposite the original images. Accordingly, we propose a hyperspectral IID method based on automatic subspace partitioning. Firstly, the hyperspectral image is divided into subspaces, and the optimal decomposition-based IID method is applied to each subspace. The reflectance intrinsic image that is obtained from the decomposition is further subjected to hyperspectral image classification processing. The experimental results obtained from this study indicate that the proposed method can considerably improve the accuracy of hyperspectral image classification.