Abstract. SegNet model is an improved model of Full Convolutional Networks (FCN). Its encoder, i.e. image feature extraction, is still a convolutional neural network (CNN). Aiming at the problem that most traditional CNN training uses error back propagation algorithm (BP algorithm), which has slow convergence speed and is easy to fall into local optimum solution, this paper takes SegNet as the research object, and proposes a method of extracting partial weights by using genetic algorithm (GA) to select features of SegNet model, and to alleviate the problem that SegNet is easy to fall into local optimal solution. In the training process of SegNet model, the weight of convolution layer of SegNet model used to extract features is optimized through selection, crossover and mutation of genetic algorithm, and then the improved SegNet semantic model (GA-SegNet model) is obtained by GA. In order to verify the image classification effect of the proposed GA-SegNet model, the same high-resolution remote sensing image data are used for experiments, and the model is compared with maximum likelihood (ML), support vector machine (SVM), traditional CNN and SegNet semantic model without GA improvement. The experimental results show that the proposed GA-SegNet model has the best classification accuracy and effect, which GA overcomes the problem of premature convergence of BP random gradient descent to a certain extent, and improves the classification performance of SegNet semantic model.
Sparse noise minimization in image classification using Genetic Algorithm and DenseNet
