Details

Autor(en) / Beteiligte

• Conceição, Marcos Reinan Assis
• de Mendonça, Luis Felipe Ferreira
• Lentini, Carlos Alessandre Domingos
• da Cunha Lima, André Telles
• Lopes, José Marques
• de Vasconcelos, Rodrigo Nogueira
• Gouveia, Mainara Biazati
• Porsani, Milton José

Titel

SAR Oil Spill Detection System through Random Forest Classifiers

Ist Teil von

• Remote sensing (Basel, Switzerland), 2021-06, Vol.13 (11), p.2044

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2021

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were oil spills, biological films, rain cells, low wind regions, clean sea surface, ships, and terrain. The second one was a SAR image oil detector named “Radar Image Oil Spill Seeker (RIOSS)”, which classified oil-like targets. An optimized feature space to serve as input to such classification models, both in terms of variance and computational efficiency, was developed. It involved an extensive search from 42 image attribute definitions based on their correlations and classifier-based importance estimative. This number included statistics, shape, fractal geometry, texture, and gradient-based attributes. Mixed adaptive thresholding was performed to calculate some of the features studied, returning consistent dark spot segmentation results. The selected attributes were also related to the imaged phenomena’s physical aspects. This process helped us apply the attributes to a random forest, increasing our algorithm’s accuracy up to 90% and its ability to generate even more reliable results.