Oil spill detection based on texture analysis: how does feature importance matter in classification?

Oil spill mapping and detection represent a relevant issue from an environmental point of view, given the effects on marine ecosystems. This study presents a new feature space assessment protocol for oil spill mapping using the Google Earth Engine (GEE). First, we selected five free Sentinel-1A sens...

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Bibliographic Details
Published in:International journal of remote sensing 2022-06, Vol.43 (11), p.4045-4064
Main Authors: Vasconcelos, Rodrigo N., Lentini, Carlos A. D., Cunha Lima, André T., Mendonça, Luís F. F., Miranda, Garcia V., Cambuí, Elaine C. B., Costa, Diego Pereira, Duverger, Soltan Galano, Gouveia, Mainara B., Lopes, José M., Porsani, Milton J.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Cloud processing
Convolution
Detection
Evaluation
Feature space
Google earth engine
Image classification
Mapping
Marine ecosystems
oil detection
oil spill
Oil spills
Pollution detection
SAR images
Sentinel images
Synthetic aperture radar
Texture
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Summary:Oil spill mapping and detection represent a relevant issue from an environmental point of view, given the effects on marine ecosystems. This study presents a new feature space assessment protocol for oil spill mapping using the Google Earth Engine (GEE). First, we selected five free Sentinel-1A sensor images from the GEE catalogue. Next, we processed the features evaluated from Gray Level Co-occurrence Matrix (GLCM) spectral and texture data. A recursive protocol that comprises a sequential classification of the evaluated image was also applied, wherein each iteration, the feature with less importance, was removed based on the Gini index. We used the Random Forest algorithm for image classification. Each image was trained on 10,000 points and evaluated for accuracy, with an equal number of points collected independently. Our results showed that the Sum Average (Savg), Convolution Smooth (Smooth), Cluster Shade Shade, and Gray level Correlation (Corr) features were essential to identify oil spills and increase the accuracy values. The best classification results based on the features removal experiment and global accuracy were Angola (0.9960), Trinidad and Tobago (0.9829), Italy (0.9506), Kuwait (0.9547), and Dubai (0.9344). Furthermore, it revealed that the protocol created was essential for better understanding the parameter space to detect oil spills with SAR images.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2022.2106163