Due to the increasing amount and diversity of remotely sensed data, image registration is becoming one of the most important issues in remote sensing. In the near future, remote sensing systems will provide large amounts of data representing multiple- time or simultaneous observations of the same features by different sensors. The combination of data from coarse-resolution satellite sensors designed for large-area survey and from finer- resolution sensors for more detailed studies will allow better analysis of each type of data as well as validation of global low-resolution data analysis by the use of local high-resolution data analysis. This integration of information from multiple sources starts with the registration of the data. The most common approach to image registration is to choose, in both input image and reference image, some well-defined ground control points (GCPs), and then to compute the parameters of a deformation model. The main difficulty lies in the choice of the GCPs. In our work, a parallel implementation of decomposition and reconstruction by wavelet transforms has been developed on a single-instruction multiple-data (SIMD) massively parallel computer, the MasPar MP-1. Utilizing this framework, we show how maxima of wavelet coefficients, which can be used for finding ground control points of similar resolution remotely sensed data, can also form the basis of the registration of very different resolution data, such as data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and from the Landsat/Thematic Mapper (TM).