Analysis of GF-2 Ancient City Wall Extraction Technology Based on Object-oriented Combined with Convolutional Neural Network
In view of the current problem that it is difficult to dynamically monitor and protect some ancient archi-tectural sites due to the limitations of terrain and climate,an object-oriented classification algorithm combined with convolutional neural network is proposed based on high-resolution images to realize the fine extraction of the walls of the large earthen ancient city of Wuweiman.Compared with the maximum entropy coupled discrete particle swarm algorithm(MEDPSO)and maximum likelihood classification(MLC),the applicability and accuracy of the method are verified.The results show that the object-oriented classification method combined with convolutional neural network shows strong anti-interference and generalization ability and can effectively extract the boundary of the city wall.Its Kappa(0.95)is higher than those of MEDPSO(0.92)and MLC(0.86).And its overall accuracy(97.46%)is higher than those of MEDPSO(95.68%)and MLC(92.67%).The effectiveness of the object-oriented classification method combined with convolutional neural network for the extraction of ancient building walls is veri-fied,which provides technical reference and reference value for the extraction of ancient city wall information.
convolutional neural networksobject-orientedmaximum entropy modeldiscrete particle swarm opti-mizationancient city wall
万方数据
维普
