Multimodal depth learning-based remote sensing image classification method

Abstract

The invention discloses a multimodal depth learning-based remote sensing image classification method. The method includes the following steps that: a multimodal remote sensing image data sample set is constructed, wherein the multimodal remote sensing image data sample set includes multimodal remote sensing images which are formed based on different imaging principles; data modal sensitive feature learning depth networks are constructed, and different modal data are adopted to train corresponding feature learning depth networks; an inter-modal feature correlation model is constructed, correlation sharing features are generated and trained; and a test sample set is inputted into a trained and finely-tuned multimodal depth network, so that precise classification of the remote sensing images can be realized. With the multimodal depth learning-based remote sensing image classification method adopted, multi-source remote sensing images are effectively utilized; inter-modal complementary and cooperation information is mined; a whole process is executed automatically; low classification precision caused by artificial input and subjective human factors is avoided; and classification precision is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a remote sensing image classification method based on multimodal deep learning. Background technique [0002] Classification is an important basis for the application of remote sensing image analysis. Based on the multi-platform, multi-spectrum, and multi-channel continuous observation of the ocean, the "star-machine-earth" three-dimensional observation network has spawned multi-scale, multi-temporal, multi-azimuth, and multi-level ocean remote sensing images, providing a richer and more accurate description of ground objects. data information. There is information complementarity and cooperation among multi-source remote sensing images. Using two or more remote sensing data sources to extract information can obtain higher extraction accuracy than using any one of the remote sensing data sources alone. Therefore, the abundant and available multi-source remote sensing i...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies in the prior art, to provide a remote sensing image classification method based on multimodal deep learning, to solve the problem of low classification accuracy caused by manual extraction of low-level features and the limitation of information contained in single-modal data. Learn to extract the high-level features of remote sensing images step by step, and effectively associate the complementary, cooperative and redundant information between multi-source remote sensing images, and then obtain high-level associated shared features to achieve accurate classification of remote sensing images

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