Layering single-class ship target false alarm eliminating method based on intra-class difference

Abstract

The invention discloses a layering single-class ship target false alarm eliminating method based on the intra-class difference. The method includes the steps that an optical remote sensing image is divided into a large ship slice, a small ship slice and a false alarm slice, a first-layer classifier is established based on large ship feature data, and a large ship to which great attention is paid in ship detection can be basically recognized through the first-layer classifier; when features of the large ship are obvious, the large ship can be detected through primary detection, and therefore the purpose of rapid large ship detection can be achieved; a large ship data set missing detection is trained, so that a second-layer classifier is formed, when the large ship is not recognized through the first-layer classifier, the large ship easily missing detection can be detected through the second-layer classifier, and therefore the detection probability can be increased; through screening of the first-layer classifier and the second-layer classifier, data with the large ship features in a small ship data set are eliminated, the data with the obvious small ship features are kept, a small ship mistakenly-distributed set is formed, and the detection probability of a small ship is increased.

Description

technical field [0001] The invention belongs to the technical field of target recognition, and in particular relates to a hierarchical single-category ship target false alarm elimination method based on intra-class differences. Background technique [0002] Ship detection plays a prominent role in national marine security, marine management, monitoring illegal fishing, etc. In recent years, with the remarkable progress of remote sensing technology, remote sensing technology has also been widely used in the field of ship detection. Among them, optical remote sensing image ship detection is of great significance. Optical remote sensing images have outstanding characteristics such as high resolution, wide coverage, and large amount of information, but they are easily affected by various factors, such as noise, shadows, and other interference. In addition, in ship detection, the background of the sea surface is relatively complex, and it will also be interfered by clouds, etc....

AI Technical Summary

Problems solved by technology

Combining the current methods can eliminate some false alarms, but it is difficult to effectively eliminate false alarms that are easily judged as ships, such as: clouds and small islands, etc.

However, the classifiers used in some methods, such as neural networks and SVMs, usually only use one layer of classifiers, and it is difficult to ensure generalization and fitting at the same time.

In the stage of ship identification, it is usually judged one by one until all layers are judged as a ship, which is not conducive to the occasion that requires the rapid output of the judged target

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