Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by the homogenous region obtained by segmenting the target image. On this basis, the deep Siamese convolutional neural network is used to reduce the distribution difference between the source domain image and the target domain image to achieve the final result of the target hyperspectral image classification. The experimental results show that the combination of homogenous region and Siamese convolutional network can improve the classification effect of semisupervised transfer learning and better solve cross-regional hyperspectral image classification.
Deep learning, neural network, transfer learning, classification, hyperspectral image, image classification
SHABAZ, MOHAMMAD and SONI, MUKESH
"Cognitive digital modelling for hyperspectral image classification using transfer learning model,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 31:
6, Article 9.
Available at: https://journals.tubitak.gov.tr/elektrik/vol31/iss6/9