Remote sensing classification aims at extracting available geographic information from image spectrum for resources and environment monitoring

but due to the spectral drift effect

the lack of effective strategies on historical sample reuse for image processing technology based on pattern classification restricts remote sensing classification accuracy with limited target samples.To solve this problem

this paper proposes a transfer learning algorithm for remote sensing classification using improved Bayesian ARTMAP neural network.More productive resonance matching is used to suppress the unattractive property of category proliferation

so that the incremental expectation maximization can be introduced to update parameters adaptively.The classification prior knowledge of the historical samples is transferred to the target model.The experimental results show that this method can effectively compensate for the lack of target training data by reusing the historical samples and significantly improve the accuracy of remote sensing image classification compared with other sample utilization strategy.