关键词: 迁移学习, 遥感图像, 识别分类, 特征融合

Abstract: Due to the lack of resampling processing for graphic features,the final classification accuracy and the classification performance are not ideal.To this end,a remote sensing image classification based on deep convolution transfer learning network algorithm is proposed.By dividing the geometric region of the original remote sensing image,extracting the descriptors for the distorted geometric pixel,combining the scale space and pixel registration and the rotation angle of the image is calculated,and the geometric correction of the remote sensing image is completed.The dual stream network architecture is innovatively adopted to splice the channel of pixel frequency bands,combining the interaction results of image consistency joint features,outputting image fusion features,and resampling the features with new pixel value index.The deep convolutional transfer learning network algorithm is used to calculate the probability of the input image in the reference class,so as to realize the remote sensing image classification.The application results of the example show that the designed method has high classification accuracy and better classification performance in remote sensing image classification.

Key words: Transfer learning, Remote sensing image, Identification and classification, Features fusion