Object Detectionin of Remote Sensing Images Based on Convolutional Neural Networks

Aiming at the problem of object detection in remote sensing images, the Faster-Renn 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 arc 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.