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Here we present a robust fully automated method for relative radiometric co-registration. First, a new low dimensional feature-point descriptor, called the Expanded Haar-Like Filter (EHLF) descriptor, is introduced. The EHLF has many desirable properties like flexible design, fast computation, and multi-scale description, while also being insensitive to variations in image quality. Next, two spatial matching schemes are proposed for increasing the percentage of correctly matched feature points. The first is based on a global affine model and the second utilizes dynamic local template fuzzy distance matching. Finally, precise pixel-to-pixel invariant feature points are extracted from a diversity of image locations centered at matched local extrema points. Experimental results show that for high-resolution multi-temporal imagery, the EHLF descriptor can obtain matched feature points with accuracies equivalent to that using a higher dimensional descriptor. In addition, the EHLF descriptor produces a larger number of correctly matched feature points. The spatial matching methods significantly improve feature-point matching, especially for image pairs with large geometric distortions. Radiometric co-registration quality based on the pixel-based invariant features was tested using four different evaluation datasets, and the results demonstrate that the proposed approach produces the lowest normalized root-mean-square error compared with six other automated methods. The proposed method depends on successful extraction of feature points, which may not be available for the scenes that are fully undeveloped (e.g., forest areas). Nonetheless, Monte Carlo simulations show that 30 to 50 correctly matched feature points will provide relatively stable radiometric calibration coefficients.