A transfer learning method for high spatial resolution remote sensing images based on deep features

Abstract

The invention provides a high spatial resolution remote sensing image transfer learning method based on depth features. This method can use existing remote sensing images and sample information to directly classify newly acquired remote sensing images, thus providing support for remote sensing rapid monitoring. The method includes the following steps: for the source domain image and the target domain image, use the principal component transformation to extract the first three principal component components respectively; for the generated new image of the three bands, extract the image block input centered on each pixel To the trained multi-layer convolutional neural network; output the last fully connected layer of the convolutional neural network to obtain the depth feature representation of the pixel; for the source domain image and the training samples of the source domain, use support based on the extracted depth features The vector machine classifier is trained to obtain a classifier; for the target domain image, use the obtained support vector classifier to directly classify, and complete the transfer learning from the source domain image and the corresponding relationship between categories to the target domain.

Description

technical field [0001] The present invention relates to remote sensing image processing technology, in particular, relates to a high spatial resolution remote sensing image transfer learning method based on depth features, the method can be based on the existing high spatial resolution remote sensing images and sample data, the newly acquired remote sensing Images are automatically classified without samples, so as to quickly process remote sensing images and provide support for remote sensing monitoring. Background technique [0002] Remote sensing technology is currently widely used in geoscience applications, such as forest resource planning, crop yield estimation, environmental assessment, disaster monitoring, etc. Remote sensing image classification technology is a key step in converting remote sensing images from data to information. In terms of whether training samples are needed, remote sensing image classification methods can be divided into supervised classificati...

AI Technical Summary

Problems solved by technology

[0005] It should be pointed out that these public classic convolutional neural networks (AlexNet, GoogleNet, VGGNet, etc.) often cannot be directly applied to remote sensing images

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