Effect of compressive imaging sparse reconstruction on remote sensing

Hang Yin; Bitao Tan; Canbin Yin; Gaoxing Li

doi:10.1117/12.2648246

14 October 2022 Effect of compressive imaging sparse reconstruction on remote sensing

Hang Yin, Bitao Tan, Canbin Yin, Gaoxing Li

Proceedings Volume 12343, 2nd International Conference on Laser, Optics and Optoelectronic Technology (LOPET 2022); 1234334 (2022) https://doi.org/10.1117/12.2648246
Event: 2nd International Conference on Laser, Optics and Optoelectronic Technology (LOPET 2022), 2022, Qingdao, China

Abstract

Compressive sensing is a promising theory that can sense data in a sub-Nyquist sampling. This theory has developed rapidly in many fields as soon as it appeared. It depends on the sparsity of the signal and senses the signal by low measurement and sparse reconstruction. In theory, sparse reconstruction is the core factor of compressive sensing theory and its application. In order to explore the feasibility of applying compressive imaging in remote sensing, this paper investigate the effect of sparse reconstruction on remote sensing images using typical datasets. The reconstruction algorithms such as convex optimization and greedy are introduced to implement the image reconstruction. The quality of reconstructed images is measured by PSNR index with respect to the different datasets and algorithms. The simulations show that the sparsely reconstructed remote sensing images keeps the image quality under different compression ratios of compressive imaging. The results verify the feasibility for applying compressive imaging in remote sensing.

Citation Download Citation

Hang Yin, Bitao Tan, Canbin Yin, and Gaoxing Li "Effect of compressive imaging sparse reconstruction on remote sensing", Proc. SPIE 12343, 2nd International Conference on Laser, Optics and Optoelectronic Technology (LOPET 2022), 1234334 (14 October 2022); https://doi.org/10.1117/12.2648246

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