What are the advantages and disadvantages of using relative vs. absolute radiometric correction?

Radiometric correction in remote sensing involves two main approaches: relative and absolute. Relative correction is simpler, faster, and doesn't require ancillary data, making it ideal for large datasets or historical images. It's effective for change detection and improving visual quality but doesn't provide physical units. Absolute correction, while more complex and time-consuming, converts pixel values to actual surface properties, enabling quantitative analysis and comparison with other data sources. It requires detailed sensor and atmospheric information but offers greater accuracy and realism.

In my experience, relative radiometric correction is pivotal for temporal analysis. It aligns varying conditions of image capture, enabling consistent study of environmental changes. This technique has been instrumental in my projects on coastal erosion and urban sprawl, where detecting subtle variations over time is crucial. While it doesn't account for absolute light values, its comparative insight is invaluable for understanding dynamic landscapes.

In my experience, absolute radiometric correction is essential for accurate earth observation. It ensures data consistency across different times and sensors, crucial for long-term environmental monitoring and change detection. While it requires meticulous calibration, the precision it affords in quantifying surface characteristics is invaluable for robust scientific analysis and informed decision-making. This method has significantly enhanced the reliability of my projects on vegetation mapping and urban heat island studies.

In my experience, relative radiometric correction is a practical choice. It streamlines processing, especially when dealing with extensive archives lacking sensor details. The technique's ability to mitigate image noise significantly aids in maintaining data consistency over time, which is crucial for longitudinal studies. This approach has consistently proven valuable in my projects, enhancing image clarity and reliability without the need for extensive sensor data.

In my experience, relative radiometric correction has limitations. It often fails to reflect true surface conditions, leading to skewed data which hampers accurate analysis. I've seen cases where this resulted in unreliable change detection, stressing the need for meticulous method selection.

Relative radiometric correction- -involves adjusting the pixel values of an image relative to each other -typically using statistical methods or reference targets -simple and computationally efficient -suitable for large datasets -may not correct for atmospheric effects or sensor variability, leading to inaccuracies in absolute measurements Absolute radiometric correction- -involves calibrating pixel values to physical units, such as reflectance or radiance, through sensor-specific models and ground truth data -ensures accurate quantitative analysis and comparability between different images and sensors -requires detailed knowledge of sensor characteristics, atmospheric conditions, and ground measurements -complex and resource-intensive