Data Processing Method for Observing Marine Environment and Underwater Targets

Dear Colleagues,

Marine data, especially environmental data and underwater target data, have immeasurable value, and exploring their enormous value will be crucial for protecting the marine environment and exploring and developing marine resources. With the continuous application of new technologies such as satellite remote sensing observation and navigation observation, a wealth of environmental and underwater target data has been collected. Effective data processing for observing the marine environment and underwater targets is beneficial for marine disaster prevention and mitigation, ecological environmental protection, marine target detection and tracking, and emergency rescue.

In the past few decades, there have been successful methods applied to the processing of marine environment and underwater target data, including mode decomposition methods, nonlinear dynamic analysis methods, and machine learning methods. Mode decomposition methods can be used to decompose complex marine data, exploring the differences between data from the mode perspective; nonlinear dynamic analysis methods can provide features that characterize the complexity of marine data; and machine learning methods can be used for data classification and identification. For example, by decomposing marine environment data such as temperature and humidity into several modes through mode decomposition, extracting the nonlinear dynamics feature of the modes, and then classifying them using machine learning methods, future changes in marine temperature and humidity can be predicted, which is beneficial for predicting climate change, studying marine ecosystems, and protecting the marine environment. Meanwhile, similar methods can be used to process underwater target data to help us understand the distribution of marine species, protect rare and endangered species, and explore deep-sea resources. However, the above methods not only have some shortcomings in themselves, such as the parameter selection problem in mode decomposition and the overfitting problem in machine learning, but also do not consider the data target characteristics under different marine conditions. Therefore, we should further explore advanced data processing methods to provide richer information about the marine environment and underwater targets for human perception and prediction of the marine world.

This research topic is devoted to studying different aspects of advanced marine data processing methods for observing the marine environment and underwater targets, from basic theory to application. Researchers from the global academic field and industry are encouraged to submit high-quality, unpublished, original research articles and review articles in a wide range of fields related to data processing for the marine environment and underwater targets. Potential topics include but are not limited to the following:

1. Machine learning in marine acidification prediction;
2. Signal processing for marine temperature data analysis;
3. Feature extraction for marine soil moisture data;
4. Noise reduction of underwater ship target data;
5. Classification and recognition of various marine organisms data;
6. Analysis of underwater sonar data by mode decomposition method;
7. Application of the nonlinear dynamic analysis method in marine environment data research.

Dr. Yuxing Li
Guest Editor

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• data processing
• marine environment
• underwater target
• machine learning
• mode decomposition
• nonlinear dynamic analysis