Few-shot urban remote sensing image information extraction method based on meta learning and attention

Abstract

A few-shot urban remote sensing image information extraction method based on meta learning and attention includes building a few-shot urban remote sensing information pre-trained model. During a pre-training stage, pre-training network learning is performed for a few-shot set to fully learn feature information of existing samples and obtain initial feature parameters and a deep convolutional network backbone of the few-shot set; the few-shot urban remote sensing information pre-trained model is a network structure including a convolutional layer, a pooling layer and a fully-connected layer, and includes five sections of convolutional network where each section includes two or three convolutional layers, and an end of each section is connected to one maximum pooling layer to reduce a size of a picture; the number of convolutional kernels inside each section is same, and when closer to the fully-connected layer, the number of convolutional kernels is larger.