  Recently, deep learning technology have been extensively used in the field of
image recognition. However, its main application is the recognition and
detection of ordinary pictures and common scenes. It is challenging to
effectively and expediently analyze remote-sensing images obtained by the image
acquisition systems on unmanned aerial vehicles (UAVs), which includes the
identification of the target and calculation of its position. Aerial remote
sensing images have different shooting angles and methods compared with
ordinary pictures or images, which makes remote-sensing images play an
irreplaceable role in some areas. In this study, a new target detection and
recognition method in remote-sensing images is proposed based on deep
convolution neural network (CNN) for the provision of multilevel information of
images in combination with a region proposal network used to generate
multiangle regions-of-interest. The proposed method generated results that were
much more accurate and precise than those obtained with traditional ways. This
demonstrated that the model proposed herein displays tremendous applicability
potential in remote-sensing image recognition.
