Automatic Eddy Extraction from Sst Imagery Using Artificial Neural Network

Mesoscale eddies have a significant impact on the exchange of material and energy in the ocean, and thus their knowledge are of great importance for the study of oceanic circulation. Images of sea surface temperature (SST) created from satellite infrared sensors are used to detect mesoscale eddies that have a surface signature in temperature. Various techniques, including texture analysis, wavelet transform, mathematical morphology, etc., have been used to identify mesoscale eddies from SST images. However, mainly due to the strong morphological variation of eddies which causes the absence of a valid analytical model, these approaches either have many limitations or are rather complex. The paper proposes a new methodology for automatic detection of mesoscale eddies from SST images using artificial neural network (ANN) and edge detection, and it can be summarized in the following steps: 1) preprocessing to reduce noise and to obtain maps of temperature gradient, its direction and magnitude; 2) using artificial neural network to detect the possible eddy centres; 3) removing the false eddy centres; 4) detecting the edge points of the eddies and fitting them into ellipses. This approach has been applied to the detection and extraction of mesoscale eddies in the Gulf Stream area using NOAA GOES 10 & 12 SST images, and the experiment has proved that this method has the following advantages: 1) It’s effective and robust with high detection accuracy (over 90%), especially for the cold-core eddy (over 95%) since the training set used for the neural network is mainly composed of cold-core eddies. If more samples of eddies and non-eddies are used to train the neural network, the detection accuracy can be further improved. 2) Not only are eddies detected by the approach, but also the parameters of eddies such as centre location, size and direction are also calculated at the same time, which can be rather useful for detecting the change of eddies in sequential SST images. 3) The procedure is rather simple, efficient and easily reconfigurable, without the need of a valid analytical model. It can be adapted to different conditions such as different sizes of eddies, different cores (cold or warm), and different resolution of SST images. Therefore, the proposed approach is rather suitable for automatically detecting and extracting eddies from satellite SST images.