In this paper, a method for solving the nonlinear problem of classifying Multi-band Remote Sensing Images is proposed. By introducing the concept of Kernel space, the classification, which cannot be performed linearly in the input space, can be mapped into a high-dimension space, in which the problem can be solved linearly. Moreover, by using Kernel Function, the complex computation in the high-dimension space can be avoided. Based on this method, this paper improved a simple classification method called adaptive min-distance algorithm, and applied it to the classification of multi-band remote sensing images. A choice heuristic is also presented to select an appropriate Kernel Function. Experiments show that, with higher accuracy achieved, the improvements prove to be useful in the classification.