Pansharpening via Detail Guided and Global Scale Convolution
| Autor: | Weisheng Li, Xudong Zhi, Yidong Peng, Yijian Hu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14687-14703 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1939-1404 2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3444003 |
| Popis: | Pansharpening is a crucial step in various remote sensing tasks, aimed at generating high-resolution multispectral images from panchromatic and low-resolution multispectral images. While deep learning has shown promising results in improving the accuracy of pansharpening, previous models often enhanced accuracy by stacking a large number of trainable parameters, making model training and application challenging. In this article, we propose a pansharpening network based on detail guided and global scale convolution, which can balance the parameter quantity of the model and its accuracy. Specifically, our model utilizes the global convolutional neural network (GCNN) module, which has favorable time complexity and, to some extent, alleviates issues such as insufficient receptive fields and excessive compression of long-distance information found in traditional convolutional neural networks. GCNN enables our model to capture global information effectively. In addition, we introduce a detail guided residual learning module that uses high-resolution image information to enhance details and compensate for the loss of high-frequency information during forward propagation. Furthermore, we design a lightweight convolutional module named channel aggregation learning that utilizes partial convolution for efficient interaction of interchannel feature information. Moreover, we introduce fast Fourier transform loss in the loss function to capture frequency domain information loss, further improving model performance. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed method. |
| Databáze: | Directory of Open Access Journals |
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