Entropy-Based Coherence Metric for Land Applications of GNSS-R

Autor:	Maurizio di Bisceglie, Marco Lavalle, Ilaria Mara Russo, Carmela Galdi, Cinzia Zuffada
Rok vydání:	2022
Předmět:	Mixing (mathematics) GNSS applications Computer science Metric (mathematics) Visual comparison General Earth and Planetary Sciences Entropy (information theory) Satellite system Land cover Coherence (statistics) Electrical and Electronic Engineering Remote sensing
Zdroj:	IEEE Transactions on Geoscience and Remote Sensing. 60:1-13
ISSN:	1558-0644 0196-2892
Popis:	A novel metric for detecting coherence in Global Navigation Satellite System Reflectometry (GNSS-R) signals is presented and evaluated. It applies the Von Neumann information entropy metric for density matrices, a powerful indicator of the degree of mixing between states, coherent and incoherent, of the scene under investigation. The metric is applied to a set of Raw IF data acquired by the CYGNSS observatories over Lake Okeechobee FL, in order to test the sensitivity of the entropy to different land cover types, including wetlands and open water. Visual comparison of results with Sentinel-1 images provides a first step in the validation of the effectiveness of entropy in detecting the presence of water covered by emergent vegetation. In addition, the entropy-based metric could be implemented on future space-based GNSS-R receivers to adapt the incoherent integration times to the observed scene, thus achieving an improvement in along-track resolution.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ad92b181ac979e73598a0862f847151b https://doi.org/10.1109/tgrs.2021.3125858 Zobrazit plný text záznamu