Design and Application of Hash Classifier Incorporating Rotation Invariant Constraint
Classifier is an important part of target recognition and detection,and target detection for remote sensing images has im-portant application value in transportation,military,agriculture,and other fields.But with the sharp increase in resolution of remote sensing images,how to improve the efficiency of target detection in remote sensing images has become a challenge.This article uti-lizes the rotation invariance characteristics of remote sensing targets and the fast classification ability of hash learning to design and implement a hash classifier that integrates rotation invariance constraints.The aim is to make remote sensing targets have similar bi-nary hash codes before and after rotation.Through experiments,it has been shown that this classifier can significantly improve the detection speed while also improving the accuracy of detection,and can be extended to other hash learning methods.