Target Detection of High-Resolution Remote Sensing Images Based on Convolutional Neural Network with Salient Features

The processing technology of remote sensing images has attracted more and more attention. Since remote sensing image target detection technology has a wide range of applications in, terrain exploration and post-disaster reconstruction, etc. Remote sensing image target detection refers to finding the target of interest in the remote sensing image and giving the specific location, while remote sensing image target recognition is the further classification of a certain target, which is a long-term concern in the field of remote sensing image processing. Convolutional Neural Network CNN (Convolutional Neural Network) has achieved great success in the field of computer vision with its deep semantic features, and in recent years, it has been increasingly applied to remote sensing image target detection and recognition tasks. Aiming at the task of remote sensing image target detection, this paper proposes a new deep feature-based remote sensing image target detection method. The depth feature extracted by CNN is used to extract the region of interest, and the target confirmation of the region of interest is carried out through multiple scales of CNN. This method does not require bounding box data for training, and improves the detection accuracy and reduces the false alarm rate.