关键词: 遥感图像, 反卷积, 高层特征, 迁移学习

Abstract:

At present, the deep-convolution neural network is used to extract the underlying features of images, and the classification effect is quite good. However, it is not fully applicable to hyperspectral remote sensing images with large data volume, multiple bands and high correlation between bands. Aiming at the Hughes phenomenon often appearing in the classification of hyperspectral remote sensing objects, that is, when the training samples are certain, the prediction ability of the model decreases with the increase of the dimension, and a remote sensing feature combining high-level feature space and migration learning network is proposed. Image classification algorithm firstly uses the two-layer stacking deconvolution network to extract the high-level features of the target data set, then uses the convolution layer weight of VGG16 model to construct the migration network model, and finally introduces the high-level features into the migration network to strengthen the training. The training model is more advantageous, and the training model can be used to classify hyperspectral remote sensing data sets more accurately. The experimental results show that in the Satellite, NWPU and UC Merced experimental data, the classification accuracy of glaciers, buildings and beaches has been effectively improved, reaching 92%. For remote sensing images of deserts, rocks, waters and other special environments, the overall classification accuracy is improved by about 5%. The underlying features and middle-level features of some hyperspectral remote sensing data are not ideal in the trainer. The increase of the band will also lead to the redundancy of information and the complexity of data processing. On the contrary, the high-level features retain the features in this part of the data. The outline of the information can be better adapted to the classifier and get better classification results.

Key words: remote sensing image, deconvolution, high-level features, migration learning