Depth feature-based high-spatial resolution remote sensing image migration learning method

Abstract

The invention provides a depth feature-based high-spatial resolution remote sensing image migration learning method. According to the method, a newly obtained remote sensing image is directly classified by using existing remote sensing images and the sample information, so that the method provides the support for the remote sensing rapid monitoring. The method comprises the following steps of carrying out the main component transformation for a source domain image and a target domain image, and respectively extracting previous three main components of the images; for newly generated images of three wave bands, extracting an image block with each pixel as the center thereof and inputting the image block into a well trained multilayer convolution neural network; outputting the last one full-connection layer of the convolution neural network and obtaining the depth feature representation of the pixel; subjecting the training samples of the source domain image and the target domain image to the support vector machine classifier training process based on extracted depth features and then obtaining a classifier; classifying the target domain image directly by using the obtained support vector classifier, and completing the migration learning process of the source domain image and the category corresponding relation 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.) are obtained by training natural target images (such as cars, airplanes and other natural common objects and various scenes), so often cannot be directly applied to remote sensing images

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