Global change research consistently requires the combined use of cross-sensor images with similar spectral wavelengths. To meet the temporal resolution and coverage requirements of remote sensing applications

new requirements are proposed for remote sensing image processing technology. Such technology requirements are related to how to obtain geometric consistency between cross-sensor/multi temporal data

how to obtain radiometric normalization

and how to obtain land cover class labels that are consistent between cross-sensor/multi temporal data. They are also related to highly automated processing. For the above requirements

we propose a framework for the automatic processing of remote sensing images with "an invariant feature point set"（IFPs） as the control data set. Specifically

we combine the spatial and temporal alignment in geometry space

radiation space

and land cover class space into a unified framework and thereby provide an indirect means to achieve fast processing. The key technologies for building IFPs are also reviewed.