Feature Combination of Pauli and H/A/Alpha Decomposition for Improved Oil Spill Detection Using SAR

Prajapati, Kinjal; Prajapati, Payal; Ramakrishnan, R.; Mahajan, Alka; Bhavsar, Madhuri

doi:10.1007/978-981-16-0507-9_12

Cited by 1 publication

(1 citation statement)

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“…It was further observed that joint use of multiple fully polarimetric features as input to the classifier could provide higher accuracy than single fully polarimetric features [31]. In our recent work [32], the selective polarimetric features of Pauli and H/A/α decomposition were combined for efficient oil spill detection and discrimination of oil spill from lookalikes.. However, very few papers have tested the sensitivity of SAR to discriminate between oil slicks of different thicknesses and at different weathering/emulsification stages.…”

Section: State-of-the-artmentioning

confidence: 83%

Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images

et al. 2022

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Oil spills on the ocean surface are a serious threat to the marine ecosystem. Automation of oil spill detection through full/dual polarimetric Synthetic Aperture Radar (SAR) images is considered a good aid for oil spill disaster management. This paper uses the power of log transformation to discern the scattering behavior more effectively from the coherency matrix (T3). The proposed coherency matrix is tested on patches of the clean sea surface and four different classes of oil spills, viz. heavy sedimented oil, thick oil, oil-water emulsion, fresh oil; by analyzing the entropy (H), anisotropy (A), and mean scattering angle alpha (α), following the H/A/α decomposition. Experimental results show that not only does the proposed T3 matrix differentiate between Bragg scattering of the clean sea surface from a random scattering of thick oil spills but is also able to distinguish between different emulsions of oil spills with water and sediments. Moreover, unlike classical T3, the proposed method distinguishes concrete-like structures and heavy sedimented oil even though both exhibit similar scattering behavior. The proposed algorithm is developed and validated on the data acquired by the UAVSAR full polarimetric L band SAR sensor over the Gulf of Mexico (GOM) region during the Deepwater Horizon (DWH) oil spill accident in June 2010.

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Section: State-of-the-artmentioning

confidence: 83%