Research On Target Detection Method Of Optical Remote Sensing Image Based On Attention Mechanism Feature Fusion Network

With the development of satellite remote sensing technology,a large number of valuable optical remote sensing satellite images have been applied to military reconnaissance,environmental monitoring and agricultural production,reflecting the important application and research value of optical remote sensing satellite data.According to the type of sensor,optical remote sensing images can be divided into visible light remote sensing images,synthetic aperture radar images and infrared remote sensing images.This thesis mainly studies the target detection of visible light remote sensing image(RGB).In the field of satellite remote sensing image processing,the target detection task of optical remote sensing image is basic and important.Especially with the development of deep learning in recent years,a large number of scholars have begun to use deep learning methods to perform target detection on optical remote sensing images.However,due to the high resolution of optical remote sensing images,there are large differences within the different categories of target.At the same time,the optical remote sensing image targets have the characteristics of multi-directional and multi-rotation.The use of deep learning to detect optical remote sensing images is still in the development stage.In order to better solve the above problems,based on the Center Net basic detection model,this thesis uses deep learning to detect the target in the visible RGB remote sensing image.The main research contents and innovations of this thesis are as follows:(1)Research on target detection method of optical remote sensing image based on attention mechanism.The Attention Model(AM)can filter the most critical and important information of the current task.The size and shape of similar targets in the optical remote sensing image are different.At the same time,the background of the optical remote sensing image is complicated,so it is difficult to distinguish between the target and the background.To solve this problem,this thesis proposes an optical remote sensing image target detection algorithm based on the attention mechanism.First,select the Hourglass-104 feature extraction network as the backbone network according to the characteristics of the optical remote sensing image target,so that it can extract more dimensional features;at the same time,introduce spatial attention mechanism and channel attention mechanism modules at each level of the feature extraction network,which can assign different weights to the features of each level of resolution,so that the network can filter out more critical information in a complex background.Through the subjective and objective analysis of the detection results and the experimental results of a variety of existing comparison algorithms,Compared with the original Center Net method,the target detection algorithm for optical remote sensing images proposed in this thesis improves the accuracy of HRRSD by 0.5 percentage point based on the attention mechanism.It can be seen that the method proposed in this thesis can better solve the large differences in the size and shape of similar targets.At the same time,the method proposed in this thesis can better solve the problem that the target and the background are difficult to distinguish.The detection effect is excellent,and it is also applicable to small-scale targets with a single background.More importantly,it is better than the other five comparison algorithms in terms of comprehensive detection effect.(2)Research on target detection method of optical remote sensing image based on multiscale feature fusion.Multi-scale feature fusion can not only improve the detection performance of small-scale targets,but also enhance the deep characterization capabilities of features.In order to solve the problem that it is difficult to detect small-scale targets in optical remote sensing images under complex backgrounds,this thesis designs a network structure based on multi-scale fusion based on(1).First,design an efficient cross-level connection of the same scale in the feature extraction network,so as to fuse more features without increasing a lot of cost;at the same time,for cross-level features of different scales in the feature extraction network,a top-up Multi-scale feature fusion is carried out in the following way to further improve the performance of the network.Through the subjective and objective analysis of the detection results and the experimental results of a variety of existing comparison algorithms,compared with the original Center Net method,the optical remote sensing image target detection algorithm proposed in this paper improves the accuracy of the HRRSD data set by 1.8% and 1.15% higher than the method in(1).It can be seen that the rich semantic information and position information of small-scale targets are extracted by the optical remote sensing image target detection algorithm based on multiscale feature fusion proposed in this thesis.For small targets with variable scales in complex backgrounds,the detection effect is excellent,and the comprehensive detection effect is better than the other five comparison algorithms.