Earth observation image generation method based on improved DCGAN
In order to study the balance of UAV ground observation image samples and improve the ap-plication of ground observation in deep learning,an image generation method is used to generate a large number of UAV ground observation images.For the stability of the image generation model during train-ing and the quality of the generated images,a ground observation image generation method based on improved DCGAN is proposed.Firstly,a batch processing layer is added to the network structure of the generator and discriminator of DC GAN;secondly,the optimizer of the discriminator is improved to sto-chastic gradient descent and the optimizer of the generator adopts adaptive learning rate,and finally,the loss function of the model is improved.The experimental results show that the data generated by the im-proved DCGAN network model is similar to the original data in terms of statistical characteristics,and the model performance is good.Compared with other GAN-derived models,the improved DCGAN model is more stable,and there is no pattern collapse during the training process,and the FID score value of the model-generated image is 4.631,which is 2.409％lower than that of the original DCGAN model,indicating that the quality of the image generated by the proposed method is very high.The FID score of the model generated image is 4.631,which is 2.409 lower than the original DCGAN model,indica-ting that the proposed method generates better the images in quality and is more suitable for large-scale Earth observation image data generation.
earth observationdeep convolutional generative adversarial networksdeep learningimage production
国家科技期刊平台
NSTL
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