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Recently, deep learning technology have been extensively used in the field ofimage recognition. However, its main application is the recognition anddetection of ordinary pictures and common scenes. It is challenging toeffectively and expediently analyze remote-sensing images obtained by the imageacquisition systems on unmanned aerial vehicles (UAVs), which includes theidentification of the target and calculation of its position. Aerial remotesensing images have different shooting angles and methods compared withordinary pictures or images, which makes remote-sensing images play anirreplaceable role in some areas. In this study, a new target detection andrecognition method in remote-sensing images is proposed based on deepconvolution neural network (CNN) for the provision of multilevel information ofimages in combination with a region proposal network used to generatemultiangle regions-of-interest. The proposed method generated results that weremuch more accurate and precise than those obtained with traditional ways. Thisdemonstrated that the model proposed herein displays tremendous applicabilitypotential in remote-sensing image recognition.

An Efficient Target Detection and Recognition Method in Aerial Remote-sensing Images Based on Multiangle Regions-of-Interest