CTCP: Cross Transformer and CNN for Pansharpening

计算机科学 人工智能 模式识别(心理学) 特征提取 卷积神经网络 多光谱图像 全色胶片 图像分辨率 计算机视觉 特征(语言学) 变压器 工程类 电压 语言学 电气工程 哲学
作者
Zhao Su,Yong Yang,Shuying Huang,Weiguo Wan,Wei Tu,Hangyuan Lu,Changjie Chen
标识
DOI:10.1145/3581783.3613815
摘要

Pansharpening is to fuse a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LRMS) image to obtain an enhanced LRMS image with high spectral and spatial resolution. The current Transformer-based pansharpening methods neglect the interaction between the extracted long- and short-range features, resulting in spectral and spatial distortion in the fusion results. To address this issue, a novel cross Transformer and convolutional neural network (CNN) for pansharpening (CTCP) is proposed to achieve better fusion results by designing a cross mechanism, which can enhance the interaction between long- and short-range features. First, a dual branch feature extraction module (DBFEM) is constructed to extract the features from the LRMS and PAN images, respectively, reducing the aliasing of the two image features. In the DBFEM, to improve the feature representation ability of the network, a cross long-short-range feature module (CLSFM) is designed by combining the feature learning capabilities of Transformer and CNN via the cross mechanism, which achieves the integration of long-short-range features. Then, to improve the ability of spectral feature representation, a spectral feature enhancement fusion module (SFEFM) based on a frequency channel attention is constructed to realize feature fusion. Finally, the shallow features from the PAN image are reused to provide detail features, which are integrated with the fused features to obtain the final pansharpened results. To the best of our knowledge, this is the first attempt to introduce the cross mechanism between Transformer and CNN in pansharpening field. Numerous experiments show that our CTCP outperforms some state-of-the-art (SOTA) approaches both subjectively and objectively. The source code will be released at https://github.com/zhsu99/CTCP.
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