Ship target identification method based on feature migration

Abstract

The invention discloses a ship target recognition method based on feature migration, and belongs to the field of ship target recognition. According to the method, the problem of poor recognition effect of the to-be-recognized target due to the fact that the to-be-recognized target is different from the known trained target data in the aspects of appearance and imaging quality in an existing methodis solved. According to the invention, HOG features of ship images with different resolutions are extracted; and, according to the transfer learning method based on spatial alignment and probabilityadaptation, the HOG features of the source domain and the HOG features of the target domain are mapped to the same feature space, then probability adaptation and instance weight adjustment are carriedout in the same feature space, and a new source domain vectorization HOG feature and a new target domain vectorization HOG feature are regenerated, the support vector machine is trained by using thenew source domain vectorization HOG feature, and target recognition is performed on the to-be-recognized image by using the trained support vector machine. The method can be applied to identificationof the ship target in the remote sensing image.

Description

Technical field

[0001] The invention belongs to the field of ship target recognition, and specifically relates to a method of ship target recognition based on feature migration. Background technique

[0002] For optical sensors, due to changes in observation position, height, etc., the resolution of the image obtained by the target will also change. As the resolution of the images obtained by the same target changes, they often follow different distributions. For traditional machine learning methods, it is usually assumed that the training data and the test data meet the same distribution, but in fact, the target that needs to be recognized in the optical remote sensing image is often the same as the known training target data in appearance, imaging quality and other characteristics The aspects are different, which leads to the inability to identify the target well, and the effect of identifying the target to be recognized is poor. Therefore, how to improve the target recogniti...

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