Pansharpening with the Multidirection Tree Ridgelet Dictionary

Abstract

In this work, we propose a novel pansharpening method based on the multidirection tree ridgelet dictionary. A pansharpened image has a wide-ranging application area, such as object detection, image segmentation, feature extraction, and so on. Remote sensing (RS) imagery contains more abundant information on surface features. In order to represent different object information, we use three main classes of different dictionaries, which can reveal the latent structure of RS image. First, RS imagery is divided into several blocks. Each block is classified as smooth, irregular, or multidirection categories. Different categories are sparsely represented in different dictionaries. Second, the smooth blocks are sparsely represented in the discrete cosine transform (DCT) dictionary. The irregular and the multidirection blocks are sparsely represented in the KSVD and multidirection tree ridgelet (MDTR) dictionary, respectively. Finally, we can obtain the fusion image by reconstructing those blocks. Some experiments are taken on three different datasets acquired by QuickBird, GeoEye, and IKONOS satellites. Experimental results show that the proposed method can reduce spectral distortion and enhance spatial information. Meanwhile, numerical guidelines outperform some related methods.