Optimized shuffle attention based Lidar signal denoising and temperature retrievals in the middle atmosphere

• Published in: Optical and quantum electronics Vol. 56; no. 7
• Main Authors: Merjora, A. Anigo, Maran, P. Sardar
• Format: Journal Article
• Language: English
• Published: New York Springer US 13.06.2024; Springer Nature B.V
• Subjects: Artificial neural networks; Characterization and Evaluation of Materials; Computer Communication Networks; Electrical Engineering; Encoders-Decoders; Laser applications; Lasers; Lidar; Middle atmosphere; Network reliability; Neural networks; Noise levels; Noise measurement; Noise reduction; Optical Devices; Optics; Photonics; Physics; Physics and Astronomy; Remote sensing; Signal to noise ratio; DCNN; Enhanced spider wasp optimization; Lidar signal denoising; Normalization; Shuffle attention
• ISSN: 1572-817X; 0306-8919; 1572-817X
• DOI: 10.1007/s11082-024-07022-1

Lidar (Light Detection and Ranging), utilizes laser based remote sensing data to measure distances and properties of objects by analysing reflected light, serving diverse applications. Lidar signals in the middle atmosphere face challenges like noise and atmospheric uncertainties. Lidar signal denoising and temperature retrievals are crucial for accurate measurements, improving data reliability in atmospheric research. Lidar signal denoising is the process of reducing unwanted noise or interference from Lidar data, enhancing accuracy and reliability. To address the issues Shuffle Attention with Encoder and Decoder based Deep Convolutional Neural Network (SAED-DCNN) is proposed. The Lidar signal undergoes preprocessing, including normalization, followed by convolution and pooling encoding in the autoencoding layer of SAED-DCNN. Leveraging Deep Convolutional Neural Network (DCNN) and encoder-decoder Framework, with shuffle attention, enhances spatial interactions for reducing Lidar signal noise. the introduction of enhanced spider wasp optimization addresses computational complexity, optimizing SAED-DCNN parameters, presenting an innovative Lidar signal enhancement approach. The suggested framework exhibits outstanding performance, achieving signal-to-noise ratio and root mean squared error of 28.135 dB and 68.113, respectively. These metrics underscores the mode's efficacy in overcoming denoising challenges, establishing it as a robust denoising solution.