An Overview of Covariance‐based Change Detection Methodologies in Multivariate SAR Image Time Series
| Autor: | Ammar Mian, Guillaume Ginolhac, Jean‐Philippe Ovarlez, Arnaud Breloy, Frédéric Pascal |
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| Přispěvatelé: | Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Sondra, CentraleSupélec, Université Paris-Saclay (SONDRA), ONERA-CentraleSupélec-Université Paris-Saclay, DEMR, ONERA, Université Paris Saclay [Palaiseau], ONERA-Université Paris-Saclay, Laboratoire Energétique Mécanique Electromagnétisme (LEME), Université Paris Nanterre (UPN), Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ANR-17-ASTR-0015,MARGARITA,Nouvelles Techniques Robustes et d'Inférences pour le Radar Adaptatif Moderne(2017) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
[STAT.AP]Statistics [stat]/Applications [stat.AP]
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing 0211 other engineering and technologies 0202 electrical engineering electronic engineering information engineering 020206 networking & telecommunications 02 engineering and technology [STAT.OT]Statistics [stat]/Other Statistics [stat.ML] ComputingMilieux_MISCELLANEOUS 021101 geological & geomatics engineering |
| Zdroj: | Change Detection and Image Time Series Analysis 1 Change Detection and Image Time Series Analysis 1, Wiley, 2021, ⟨10.1002/9781119882268.ch3⟩ Change Detection and Image Time Series Analysis 1, 1, Wiley, 2021, ⟨10.1002/9781119882268.ch3⟩ |
| DOI: | 10.1002/9781119882268.ch3⟩ |
| Popis: | International audience; Change detection (CD) for remotely sensed images of the Earth has been a popular subject of study in the past decades. With the increase in the number of spatial missions with embedded synthetic aperture radar (SAR) sensors, the amount of readily available observations has now reached the "big data" era. This chapter introduces several families of elliptical distributions that can be used to model multivariate SAR images. It describes several dissimilarity functions based on covariance matrices, which are then compared for CD on the considered datasets. The chapter presents an extension of a statistical detection methodology that allows us to account for low-rank structures in the covariance matrix, whose interest is also illustrated on the real dataset. The Kullback–Leibler divergence is a popular measure between probability density functions, encountered notably in SAR change detection problems. The local covariance matrix of pixel patches appears to be relevant feature to analyze multivariate image time series. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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