In this paper, we present improved slanted edge methods of measuring the modulation transfer function (MTF) based on structured total least L1-, L2-norm edge fitting for urban remote sensing images. The structured total least L1-, L2-norm methods are used to establish slanted edge fitting models, which take the errors in both the design matrix and observation vector in the fitting model into consideration. The slanted edge fitting parameters are estimated under the two norm criteria of L1 and L2. The proposed methods are applied to both simulated and actual images. The results showed that the edge fitting parameters and MTF values calculated by the proposed methods are closer to the true values than those obtained by the traditional slanted edge method based on classical least-squares fitting. It is also found that when the data contain a large amount of noise, the structured total least L1-norm edge fitting has the greatest robustness.