Addressing the effects caused by ecological differences, which result in different spectral values for the same or similar features. For instance, varying reflectance from the same species of eucalyptus across other areas of Ethiopia due to altitudinal and ecological characteristics occurs even when data is captured by the same sensor on the same date and during the same season, but from different scenes.
Managing spectral divergence in remote sensing involves calibrating sensors, reducing noise, correcting data, and using algorithms for accurate analysis. This improves data quality for tasks like classification and monitoring changes.
You will find a complete answer in the published work: "Some notes on the divergence and transformed divergence formulae in the remote sensing literature",
https://www.researchgate.net/profile/Jordi-Cristobal-Rossello/publication/385333480_Some_notes_on_the_divergence_and_transformed_divergence_formulae_in_the_remote_sensing_literature/links/67222083ecbbde716b4bc71e/Some-notes-on-the-divergence-and-transformed-divergence-formulae-in-the-remote-sensing-literature.pdf?origin=publicationDetail&_sg%5B0%5D=j0S78waMDjTvoUk6vSbOVDvdgIBknHoyOsJaJhAgpweaW4HiVepQ7we53PyxlrH7pquUYLqnppI6epzY8q5ORw.h16VubiOJcEXDVxIPGod0dbcKfVE_JnYWrNIfeyrClMIzcbSuKyGgdE8S8jW6zdTiy_vYUXVJxOb8KRPZf21sQ&_sg%5B1%5D=MqCh7CaU_UdOA0SJ9Rp8heaOwqDajk-GiaCrxBSov5e8qR-msY2GV0jSNjkcDk9u-sgpKacLI7HpOubKHV1uHu8BG-D-jmUi9U1nYdZyI-p2.h16VubiOJcEXDVxIPGod0dbcKfVE_JnYWrNIfeyrClMIzcbSuKyGgdE8S8jW6zdTiy_vYUXVJxOb8KRPZf21sQ&_iepl=&_rtd=eyJjb250ZW50SW50ZW50IjoibWFpbkl0ZW0ifQ%3D%3D&_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIiwicG9zaXRpb24iOiJwYWdlSGVhZGVyIn19
Managing spectral divergence in remote sensing data involves addressing variations in spectral signatures due to factors like atmospheric conditions, sensor characteristics, and surface material differences. Here are key steps to manage it effectively:
1. Preprocessing
- Atmospheric Correction: Use algorithms like FLAASH or 6S to remove atmospheric effects.
- Radiometric Calibration: Convert raw digital numbers (DN) into radiance or reflectance values for consistency across images.
- Geometric Correction: Align images spatially to ensure pixel-level correspondence.
2. Spectral Normalization.
3. Dimensionality Reduction
4. Feature Selection
5. Spectral Unmixing
6. Classification and Clustering
7. Validation and Cross-Comparison
- Badges
- Science topic