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This letter proposes a novel pixel-level adaptive remote sensing image fusion method based on multicontourlet transform. The multicontourlet that we constructed is a flexible multiscale and multidirection image decomposition. With better direction selectivity and energy convergence compared to that of a multiwavelet, a multicontourlet is suitable for representing remote sensing images bearing abundant detailed and directional information. The fusion weight of the low-pass coefficients is selected adaptively based on the golden section algorithm. For the high-frequency directional coefficients, the local energy feature is employed to select the better coefficients to fusion. Experimental results show that the proposed method achieves better visual quality and objective evaluation indexes than a wavelet-transform-based, a contourlet-transform-based, and a multiwavelet-transform-based weighted fusion method.