Research On Terrain Recognition Based On Deep Learning

With the popularity of computer,data with the appearance of the 5G network also shows a tendency of the surge,traditional shallow learning algorithm under large sample data show the computation ability and the training effect is some shortcomings,deep learning network structure,is composed of multilayer neural network has strong ability of data fitting,for a large number of complex data have strong learning ability,can better solve the shallow learning algorithm ability to cope with complex data to study the problem of insufficient,in recent years,the deep learning algorithm combined with the shallow learning algorithm research hot spot.In this paper,deep belief network(DBN)and support vector machine(SVM)are studied and applied to terrain recognition.The main research contents and opinions are as follows:(1)The theory and features of image recognition are analyzed,and the current methods of image feature extraction and image classification are introduced in detail.And the principle of support vector machine,the principle of local binary mode(LBP)method,and the combination with deep belief network.At the same time,the main evaluation criteria of the current classification algorithm are studied.(2)The methods of deep belief network(DBN)and support vector machine(SVM)are applied in the field of terrain recognition and improvements are proposed.First,the image features are extracted through local binary mode,and the extracted features are passed as input data into the DBN network structure constructed initially for learning to obtain more advanced feature expression.Then,the output data of the hidden layer in the last layer of DBN is learned as the input data of SVM.OUTEX and VSPECT data sets were used for experimental data,and it was verified that the combination of deep belief network and support vector machine had better accuracy than the traditional shallow learning algorithm.(3)The sparse coding and deep belief network algorithm are combined and applied in the field of terrain recognition and improvements are proposed.In the image recognition algorithm,sparse coding(SC)can remove some redundant information in the whole image,to simplify the operation process and improve the algorithm performance.At the same time,sparse coding can improve the accuracy of image recognition when processing some nonlinear data,and it can be applied in the field of image recognition after combining it with deep belief network.OUTEX and VSPECT data sets were used to verify the validity of the deep belief network algorithm combined with sparse coding.(4)Combined with the depth belief network algorithm of random retreat and applied to the field of terrain recognition and proposed improvements.Dropout is usually used when large network structures are trained with small data sets.When the sample data is small,the network model obtained by deep belief network training may lead to local convergence or overfitting.An algorithm combining deep belief network and dropout was proposed for small samples.OUTEX and VSPECT data sets were used for experimental data,and the effectiveness of the algorithm combined with random withdrawal was verified through experiments.