Method and system for detecting oil slicks in radar images

A non-transitory computer-readable storage medium comprising instructions which, when executed by a processor, configure said processor to carry out a phase of predicting the presence or absence of an oil slick in a target image, acquired by a spaceborne or an airborne radar, by applying a convolutional network on said target image,

• wherein the convolutional network is previously trained, during a phase of training, by using a set of training images which comprises training images without oil slicks and training images with oil slicks,
• wherein the phase of training of the convolutional network uses a loss function which combines a weighted-cross-entropy loss function and a Jaccard loss function,
• wherein applying the convolutional network comprises applying an encoder comprising successive convolutional layers and downsampling layers, wherein each convolutional layer applies at least one convolution and at least one activation using an activation function,
• wherein each activation of a convolutional layer of the encoder uses a leaky rectified linear unit which applies an activation function ∫(x) expressed as:
• f
• ⁡
• (
• x
• )
• =
• {
• x
• if
• ⁢
• x
• >
• 0
• ax
• otherwise
• wherein α is a predetermined positive weighting factor, the value of which is lower than or equal to 0.001.

A system for detecting an oil slick in a target image acquired by a spaceborne or an airborne radar, wherein said system comprises a processing circuit configured to carry out a phase of predicting the presence or absence of an oil slick in the target image by applying a convolutional network on said target image,

• wherein the convolutional network is previously trained, during a phase of training, by using a set of training images which comprises training images without oil slicks and training images with oil slicks,
• wherein the phase of training of the convolutional network uses a loss function which combines a weighted-cross-entropy loss function and a Jaccard loss function,
• wherein applying the convolutional network comprises applying an encoder comprising successive convolutional layers and downsampling layers, wherein each convolutional layer applies at least one convolution and at least one activation using an activation function,
• wherein each activation of a convolutional layer of the encoder uses a leaky rectified linear unit which applies an activation function ∫(x) expressed as:
• f
• ⁡
• (
• x
• )
• =
• {
• x
• if
• ⁢
• x
• >
• 0
• ax
• otherwise
• wherein α is a predetermined positive weighting factor, the value of which is lower than or equal to 0.001.