Pan-sharpening for compressed remote sensing images

Yixiao Liu; Gang Liu; Chao Ren; Qizhi Teng; Xiaohai He

doi:10.1117/1.JRS.15.036504

23 July 2021 Pan-sharpening for compressed remote sensing images

Yixiao Liu, Gang Liu, Chao Ren, Qizhi Teng, Xiaohai He

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 15, Issue 3, 036504 (July 2021). https://doi.org/10.1117/1.JRS.15.036504

Abstract

Pan-sharpening is an effective method to obtain high-resolution multispectral (MS) images by fusing panchromatic (PAN) images with fine spatial structure and low-resolution MS images with rich spectral information. Many methods have been recently developed based on convolutional neural networks (CNNs) for pan-sharpening, which try to achieve better fusion performance using a large amount of training data. The huge demand for data of CNN-based methods results in huge pressure on transmission and storage conditions. Especially, PAN images occupy the main amount of data space because of whose spatial resolution is usually four times that of the corresponding MS images, which limits the application of pan-sharpening. We propose a CNN-based pan-sharpening method for compressed remote sensing to overcome this limitation, which can achieve pan-sharpening based on compressed data while eliminating compression artifacts. Specifically, we use compressed PAN images as the input and remove the compression artifacts, then fuse the PAN and MS images to achieve pan-sharpening. This method can greatly reduce the data capacity by data compression and ensure the pan-sharpening performance. Compared with the widely used state-of-the-art pan-sharpening approaches in a comprehensive evaluation, our method can obtain more promising results on compressed remote sensing images, and its superiority is thus demonstrated.

Citation Download Citation

Yixiao Liu, Gang Liu, Chao Ren, Qizhi Teng, and Xiaohai He "Pan-sharpening for compressed remote sensing images," Journal of Applied Remote Sensing 15(3), 036504 (23 July 2021). https://doi.org/10.1117/1.JRS.15.036504

Received: 29 April 2021; Accepted: 12 July 2021; Published: 23 July 2021

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