Remote sensing image fusion based on morphological filter and convolutional sparse representation

Yuting Liu; Fan Liu

doi:10.1117/12.2603175

2 July 2021 Remote sensing image fusion based on morphological filter and convolutional sparse representation

Yuting Liu, Fan Liu

Proceedings Volume 11887, International Conference on Sensors and Instruments (ICSI 2021); 118870M (2021) https://doi.org/10.1117/12.2603175
Event: International Conference on Sensors and Instruments 2021, 2021, Qingdao, China

Abstract

Remote sensing image fusion is the process of obtaining high-resolution multispectral images by fusing spectral information-rich multispectral images and spatial information-rich panchromatic images. Sparse representation has achieved good results in this field, but the sparse representation is encoded in blocks, which destroys the correlation between image blocks and thus causes the problems of spectral distortion and missing details in the fusion results. To address the above problems, a fusion algorithm combining convolutional sparse representation and morphological filter is proposed. The convolutional sparse representation can represent the whole image sparsely, which fully considers the correlation between pixels and reduces the spectral distortion of the fusion results. The morphological filter can estimate the spatial details of the image more accurately, so that more spatial detail information can be obtained in the fusion result. And the methods based on multiplicative injection is used, aiming to inject more detailed information into the fusion results. The experimental results show that the objective evaluation index of the fusion results obtained by this method is better, and the subjective visual effect is better.

Citation Download Citation

Yuting Liu and Fan Liu "Remote sensing image fusion based on morphological filter and convolutional sparse representation", Proc. SPIE 11887, International Conference on Sensors and Instruments (ICSI 2021), 118870M (2 July 2021); https://doi.org/10.1117/12.2603175

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