Hyperspectral remote sensing images (HRSIs) face the problem of extracting efficient discriminant features when foreign objects with similar spectra and outlier classes exist. For solving the problem, first, we derive a class pair (CP) form of the classical Fisher criterion, namely CP Fisher criterion, where the between-class scatter matrix and within-class scatter matrix are both decomposed into the form of CPs. And then, a CP-weighted criterion is proposed to weight the between-class scatter matrix and within-class scatter matrix of each CP according to its separability so that the separabilities of CPs are balanced in the CP-weighted feature subspace. The Bayesian classifier and k-nearest neighbors classifier are used to evaluate the feature extraction performance. Experimental results on three real HRSIs show that the presented CP-weighted subspace method improves the classification accuracies of the CPs with small separabilities so that it is superior to the linear discriminant analysis subspace method and the weighted pairwise Fisher subspace method.