Details

Autor(en) / Beteiligte

• Zhang, Yongjun
• Liu, Chi
• Sun, Mingwei
• Ou, Yangjun

Titel

Pan-Sharpening Using an Efficient Bidirectional Pyramid Network

Ist Teil von

• IEEE transactions on geoscience and remote sensing, 2019-08, Vol.57 (8), p.5549-5563

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2019

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Pan-sharpening is an important preprocessing step for remote sensing image processing tasks; it fuses a low-resolution multispectral image and a high-resolution (HR) panchromatic (PAN) image to reconstruct a HR multispectral (MS) image. This paper introduces a new end-to-end bidirectional pyramid network for pan-sharpening. The overall structure of the proposed network is a bidirectional pyramid, which permits the network to process MS and PAN images in two separate branches level by level. At each level of the network, spatial details extracted from the PAN image are injected into the upsampled MS image to reconstruct the pan-sharpened image from coarse resolution to fine resolution. Subpixel convolutional layers and the enhanced residual blocks are used to make the network efficient. Comparison of the results obtained with our proposed method and the results using other widely used state-of-the-art approaches confirms that our proposed method outperforms the others in visual appearance and objective indexes.