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Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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ICESat-2 laser data denoising algorithm based on a back propagation neural network.

Wenjun Meng, Jie Li, Qiuhua Tang

|October 18, 2022

Summary

This summary is machine-generated.

A new denoising algorithm using a back propagation (BP) neural network improves the accuracy of bathymetry from Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon data in shallow island reef areas.

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Area of Science:

Geospatial science
Remote sensing
Oceanography

Background:

Satellite-based LiDAR, specifically Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon data, is valuable for surveying and mapping.
Denoising ICESat-2 data in shallow island reef areas is challenging due to weak signal strength, impacting bathymetry precision.

Purpose of the Study:

To develop and evaluate a novel back propagation (BP) neural network-based denoising algorithm tailored for ICESat-2 photon data in shallow island reef environments.
To enhance the accuracy of bathymetry derived from ICESat-2 data in these specific geographical areas.

Main Methods:

A horizontal elliptical search area was defined for photons within the ICESat-2 dataset.
Feature values were selected from the search area to train a back propagation (BP) neural network.
The algorithm's generality was tested using geographically dispersed daily and nightly data.

Main Results:

The proposed BP neural network denoising algorithm demonstrated superior performance in shallow island areas.
Results showed improvement compared to official confidence labels, the DBSCAN algorithm, and manual visual interpretation.
The algorithm effectively processed weak signals characteristic of ICESat-2 data in these regions.

Conclusions:

The BP neural network-based denoising algorithm is effective for processing ICESat-2 photon data in shallow island reef areas.
This method offers a significant improvement in bathymetry precision for challenging nearshore environments.
The developed algorithm provides a reliable solution for enhancing the quality of satellite-derived bathymetry.