Add `tropo_local_texture.py` for tropo correction (Yang et al., 2024)

[Yohcy]

Description of proposed changes

This code is based on this work:

• Yang, Q., Yunjun, Z., Wang, R. Y. (2024). Heterogeneous InSAR Tropospheric Correction Based on Local Texture Correlation, IEEE Transactions on Geoscience and Remote Sensing, 62, doi:10.1109/TGRS.2024.3356749.

This code only completes the low resolution part of this work. With the help of Yang Qingyue, it successfully gets almost the same result as in the work above. Some figures during the correction process are shown below:

• Slope Esitimation
• Slope Interpolation
• Intercept Filtering
• Estimated Low-resolution Tropo Delay

Low-resolution correction in Yang' work:

Before corrections Date: February 3, 2015 and August 21, 2017

After corrections Date: February 3, 2015 and August 21, 2017

Reminders

• [ ] Fix #xxxx
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• [ ] Make sure that your code follows our style. Use the other functions/files as a basis.
• [ ] If modifying functionality, describe changes to function behavior and arguments in a comment below the function declaration.
• [ ] If adding new functionality, add a detailed description to the documentation and/or an example.

Summary by Sourcery

Add a new tropospheric correction method using local texture correlation, based on Yang et al.'s 2024 research. Update the configuration and workflow to support this method, and provide documentation for its usage.

New Features:

• Introduce a new tropospheric correction method based on local texture correlation, as described in Yang et al., 2024.

Enhancements:

• Add support for the 'local_texture' method in the tropospheric delay correction workflow.

Documentation:

• Include documentation for the new 'local_texture' method, detailing its parameters and usage.

Summary by Sourcery

Add a new tropospheric correction method using local texture correlation, based on Yang et al.'s 2024 research. Update the configuration and workflow to support this method, and provide documentation for its usage.

New Features:

• Introduce a new tropospheric correction method based on local texture correlation, as described in Yang et al., 2024.

Enhancements:

• Add support for the 'local_texture' method in the tropospheric delay correction workflow.

Documentation:

• Include documentation for the new 'local_texture' method, detailing its parameters and usage.

[welcome[bot]]

💖 Thanks for opening this pull request! Please check out our contributing guidelines. 💖 Keep in mind that all new features should be documented. It helps to write the comments next to the code or below your functions describing all arguments, and return types before writing the code. This will help you think about your code design and usually results in better code.

[sourcery-ai[bot]]

Reviewer's Guide by Sourcery

This PR introduces a new tropospheric delay correction method based on local texture correlation, implementing the low-resolution component of Yang et al.'s 2024 work. The implementation includes slope estimation, interpolation, and intercept filtering steps to correct tropospheric delays in InSAR time series data.

Sequence diagram for tropospheric delay correction process

sequenceDiagram
    participant User
    participant CLI
    participant TropoLocalTexture
    User->>CLI: Run tropo_local_texture.py
    CLI->>TropoLocalTexture: run_tropo_local_texture(inps)
    TropoLocalTexture->>TropoLocalTexture: read_topographic_data(geom_file)
    TropoLocalTexture->>TropoLocalTexture: estimate_local_slope(dem, ts_data, inps, n_ref, meta)
    TropoLocalTexture->>TropoLocalTexture: slope_interpolation(ts_data, inps, k_htc)
    TropoLocalTexture->>TropoLocalTexture: intercept_filtering(dem, ts_data, inps, k_htc_interp, meta)
    TropoLocalTexture-->>CLI: Return corrected data
    CLI-->>User: Output corrected timeseries file

Class diagram for the new tropospheric correction method

classDiagram
    class TropoLocalTexture {
        +read_topographic_data(geom_file)
        +estimate_local_slope(dem, ts_data, inps, n_ref, meta)
        +slope_interpolation(ts_data, inps, k_htc)
        +intercept_filtering(dem, ts_data, inps, k_htc_interp, meta)
        +run_tropo_local_texture(inps)
    }
    class CLI {
        +create_parser(subparsers)
        +cmd_line_parse(iargs)
        +main(iargs)
    }
    TropoLocalTexture <|-- CLI
    note for TropoLocalTexture "This class implements the new tropospheric correction method based on local texture correlation."

File-Level Changes

Change	Details	Files
Added new tropospheric correction method using local texture correlation	Created new module for local texture-based tropospheric correction Implemented slope estimation using texture correlation Added slope interpolation for full-scale coverage Implemented intercept filtering for final correction	src/mintpy/tropo_local_texture.py src/mintpy/cli/tropo_local_texture.py
Updated configuration system to support the new correction method	Added 'local_texture' as a new tropospheric correction method option Added configuration parameters for window size and overlap ratio Set default values for new parameters (window size: 141, overlap ratio: 0.4)	src/mintpy/defaults/smallbaselineApp.cfg src/mintpy/defaults/smallbaselineApp_auto.cfg
Integrated new correction method into existing workflow	Added support for local texture method in timeseries filename generation Updated tropospheric delay correction workflow to handle the new method	src/mintpy/smallbaselineApp.py

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[codeautopilot[bot]]

PR summary

This Pull Request introduces a new method for tropospheric correction in InSAR data processing, based on local texture correlation as described in Yang et al.'s 2024 research. The new method, implemented in tropo_local_texture.py, aims to improve the accuracy of tropospheric delay corrections by leveraging high-frequency texture to estimate local slopes. The PR updates the configuration files and workflow to support this new method and includes documentation for its usage. The changes are expected to enhance the precision of InSAR data analysis by providing an alternative correction method that can be particularly useful in heterogeneous environments.

Suggestion

1. Ensure comprehensive testing of the new method across various datasets to validate its effectiveness and robustness.
2. Consider adding more detailed comments and documentation within the code to facilitate understanding and future maintenance.
3. Evaluate the performance impact of the new method, as the additional computations for local texture correlation might increase processing time.
4. Provide a comparison of results with existing methods to highlight the improvements or differences achieved by the new approach.

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