mCENTRIST, a new multi-channel feature generation mechanism for recognizing scene categories, is proposed in this paper. mCENTRIST explicitly captures the image properties that are encoded jointly by two image channels, which is different from popular multi-channel descriptors. In order to avoid the curse of dimensionality, tradeoffs at both feature and channel levels have been executed to make mCENTRIST computationally practical. As a result, mCENTRIST is both efficient and easy to implement. In addition, a hyper opponent color space is proposed by embedding Sobel information into the opponent color space for further performance improvements. Experiments show that mCENTRIST outperforms established multi-channel descriptors on four RGB and RGB-NIR datasets, including aerial orthoimagery, indoor and outdoor scene category recognition tasks. Experiments also verify that the hyper opponent color space enhances descriptors’ performance effectively.