Remote sensing image fusion method based on multiscale morphological component analysis
Abstract. A remote sensing image (RSI) fusion method based on multiscale morphological component analysis (m MCA) is presented. Our contribution describes a new multiscale sparse image decomposition algorithm called m MCA, which we apply to RSI fusion. Building on MCA, m MCA combines curvelet transform bases and local discrete cosine transform bases to build a multiscale decomposition dictionary, and controls the entries of the dictionary to decompose the image into texture components and cartoon components with different scales. The effective scale texture component of high resolution RSI and the cartoon component of multispectral RSI are selected to reconstruct the fusion image. Compared with state of the art fusion methods, the proposed fusion method obtains higher spatial resolution and lower spectral distortion with reduced computation load in numerical experiments.