Optical remote sensing image target detection method based on Bayesian transfer learning

Abstract

The invention provides an optical remote sensing image target detection method based on Bayesian transfer learning. The method is characterized in that the knowledge contained in a pre-trained target detector on a source data set is represented by using a Fisher information matrix; the Fisher information matrix is utilized to construct a target function of a target detector, and in the training process, the item participates in learning of the target detector on a target data set, so that the learned knowledge is reserved to a certain extent, and the detection precision is improved. Compared with other existing optical remote sensing image target detection algorithms, according to the method, under the premise that no extra to-be-learned parameter is introduced, the optical remote sensing image target detection precision is effectively improved, the human interpretation can be efficiently and accurately assisted, and the manpower is saved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an optical remote sensing image target detection technology, in particular to an optical remote sensing image target detection method based on Bayesian transfer learning. Background technique [0002] Optical remote sensing image target detection is to judge whether an optical remote sensing image contains interesting targets such as aircraft, vehicles, ports, etc., and successfully locate and identify them. As one of the basic tasks in the field of remote sensing image analysis, it has important application value in environmental monitoring, land use, urban planning, transportation, military and other fields. However, due to the influence of external factors such as different shooting angles, complex background components, illumination and shadow changes, it is a great challenge to carry out target detection tasks on optical remote sensing images. In order to ove...

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Problems solved by technology

But is there a sound mathematical explanation behind it? Is the knowledge contained in it fully utilized, that is, can its prediction accuracy be further improved? These issues still need further research

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