Robust oil leakage sea area identification method

Abstract

The invention relates to a robust oil leakage sea area identification method. A classifier is constructed through a cost-sensitive structural risk minimization model, and a robust oil leakage sea areaidentification method is designed for the unbalanced classification problem that the difference between the number of positive samples and the number of negative samples is too large and the common label exception problems that a large number of labels are lost, and a large number of wrong labels exist and the like in an oil leakage sea area identification task. The robust oil leakage sea area identification method can deal with the situation that the types of the complete polarization synthetic aperture radar samples in the oil leakage sea area are unbalanced, can improve the classificationprecision of the classification problem of label abnormal data, and can meet the actual requirements of the oil leakage sea area identification problem. The robust oil leakage sea area identificationmethod overcomes the problem of class imbalance caused by too large difference between the number of normal sea surface samples and the number of oil leakage sea surface samples, also overcomes the problems that an existing classification algorithm is difficult to process a large number of label missing, has a large number of wrong labels and other label anomalies, and can be effectively used foridentification and classification of offshore oil leakage areas and other practical application problems.

Description

Technical field [0001] The invention belongs to the cross field of machine learning and remote sensing information, and relates to a method of data mining and data processing, and in particular to a robust oil spill sea area identification method based on a ramp loss function. Background technique [0002] Classification problem is a classic problem in the field of data mining and machine learning. With the rapid development of e-commerce, social media, mobile Internet, satellite remote sensing and other technologies, more and more data are continuously generated. Unbalanced category data is a typical problem in classification tasks, and the task of identifying oil spilled sea areas is a problem of unbalanced categories. In the identification task of oil spill area on the sea surface, most of the sea surface is normal sea surface, and only a small part of the sea surface belongs to the oil spill area. It is difficult for ordinary identification methods to obtain better identific...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of identifying the oil spill sea area with a large difference in the number of positive and negative samples, as well as the common problem of label anomalies such as a large number of missing labels and a large number of wrong labels in this task, and proposes a robust method based on the ramp loss function. Imbalanced Classification Method

Images