Detecting changes in land cover through time using remotely sensed imagery is a powerful application that has seen increased use as imagery has become more widely available and inexpensive. Before a time series of remotely sensed imagery can be used for change detection, images must first be standardized for effects outside of real surface change. This thesis established a validation protocol to evaluate the effectiveness of an automated technique for normalizing temporally separate but spatially coincident imagery. Using the concept of pseudo-invariant features between master-slave image pairs, spatially coincident dark and bright points are identified from images and a regression equation is calculated to normalize slave images to a master. I used two sets of imagery to test the performance of the standardization process, a spatially coincident, but temporally variable time series, and spatially and temporally variable images. I tested the underlying statistical assumptions of this approach, and performed simple image subtraction to validate the reduction of master-slave differences using invariant locations. In addition I tested the possibility of reducing between-sensor differences by applying simple linear regression to comparable bands of MSS and TM sensors. Image subtraction showed decreases in master-slave differences as a result of the standardization process, and the process behaved appropriately when there should be no difference between master and slave images (adjacent, but temporally identical imagery). I also found that comparable bands between MSS and TM sensors are similar enough that linear regression may not significantly reduce between-sensor differences.