Remote sensing object detection method based on few samples

Abstract

The invention discloses a remote sensing object detection method based on a few samples, and the method comprises the steps: firstly carrying out the processing of collected samples, and obtaining anauxiliary data set; secondly carrying out the similarity verification of the auxiliary data set and a target domain; thirdly training DPM (Deformable Part based Model) parameters according to the auxiliary data set, obtaining a target classifier, and carrying out the target detection, wherein the parameters in the model are divided during the training of the DPM parameters, and then the transfer learning is carried out to obtain the target classifier. The method employs a target clustering mode during the training of a source domain data mixed model, can effectively reduce the training errorsand can reduce the training complexity. Moreover, a structural parameter dividing method is employed, so the method can improve the detection performances of an adaptive model.

Description

technical field [0001] The invention relates to the field of remote sensing target detection, in particular to a remote sensing target detection method based on a small number of samples. Background technique [0002] Remote sensing target detection is the process of accurately locating or identifying one or more types of objects in aerial images and videos through machine learning methods and computer vision algorithms. It is an advanced task in computer vision and is often used as the research basis for activity analysis, event recognition, scene understanding and object tracking. With the development of high-resolution remote sensing imaging technology in China, remote sensing target detection will play an increasingly important role in the future. [0003] However, the traditional target detection technology needs to label a large number of samples from the application data for the training of the detection classifier, so that the algorithm can approach the real distrib...

AI Technical Summary

Problems solved by technology

However, in the field of remote sensing applications, the acquisition of target samples has great limitations

First of all, it is very time-consuming and laborious to manually label large-scale image data; secondly, for some specific targets in remote sensing scenes, there are only a few samples in large-scale image data, and it is not enough for complete training. data set

[0004] However, in remote sensing target detection, there is a problem of a small number of samples. It is difficult to directly obtain a large number of samples in remote sensing images. The reason is that the targets in remote sensing ima

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