摘要
针对遥感图像中的目标检测问题,采用基于卷积神经网络的目标检测框架对目标进行提取,针对该网络制作了包含三类遥感图像中常见目标的目标检测数据集。为了解决遥感图像目标旋转角度较大的问题,将空间变换网络融入超快区域卷积神经网络,提出了一种具有旋转不变性自学习能力的目标检测模型。通过与传统的目标检测方法进行对比分析,探究了不同方法对遥感图像目标检测的实际效果。相对于传统的目标检测方法,融合了空间变换网络的卷积神经网络所提取的特征具有更好的旋转不变特性,从而能够达到更高的检测精度。
Aiming at the problem of object detection in remote sensing images, the Faster-Rcnn network based on the convolutional neural network models is used to extract the features of the object area. An object detection dataset containing three kinds of common targets in remote sensing images is made to train this network. In addition, in order to solve the problem of large rotation angle of remote sensing images, a target detection model with a rotation invariance self-learning ability is proposed, which integrates the spatial transformation network into the Faster R-CNN framework. By the analysis and comparison with the traditional object detection methods, the true effects of object detection in remote sensing images by different methods are explored. The features extracted by the convolutional neural networks based on the spatial transformation networks possess stronger orientation robustness than those by the traditional methods, which makes it possible to obtain a high detection precision.
作者
欧攀
张正
路奎
刘泽阳
Ou Pan;Zhang Zheng;Lu Kui;Liu Zeyang(School of Instrumentation Science and Opto-Electronic Engineering, Beihang University, Beijing 100191, China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第5期66-72,共7页
Laser & Optoelectronics Progress
关键词
图像处理
卷积神经网络
空间变换网络
目标检测
深度学习
image processing
convolutional neural networks
spatial transformation networks
object detection
deep learning
