Traditional change detection of land use and land cover using multitemporal satellite optical imaging sensors requires two essential steps: 1. normalization of images acquired on different dates by unchanged pixels and 2. detection of changed areas from normalized images by rapid change measurements. In image normalization, finding the unchanged pixels by extracting the pseudo-invariant features has been a widely adopted approach. Rapid change detection analyses are known as useful methods in a wide field of applications, such as disaster management and urban land management, in which multitemporal satellite imageries of the same area taken at two or more different time steps are compared in a rapid fashion over the time horizon to identify changes. The challenge, however, is to find a proper reference basis for detecting changes in a rapid fashion while maintaining a reasonable computational load. In response to the acute need for rapid change detection, this study proposes an innovative method to identify a set of pseudo-variant features (PVFs) corresponding to changed pixels via a fast search algorithm. Once PVFs are found, they may be employed as a reference basis to detect the changed objects at the ground level. The experimental results show that the proposed PVFs method can offer a quality reference basis for detecting changes based on multitemporal satellite imageries in a rapid fashion to meet various managerial goals in the field.