Multi-modal remote sensing image high-rise feature fusion classification method based on deep learning

Abstract

The invention relates to a multi-modal remote sensing image high-rise feature fusion classification method based on deep learning. The method includes the steps of preprocessing a multi-mode remote sensing data set, manufacturing a training set, a verification set and a test set of a high-rise feature extractor to construct the model parameters of the high-rise feature extractor; constructing a model of the multi-mode high-rise feature extractor to extract high-rise features of a plurality of modal remote sensing images, extracting and storing the high-rise features of the multi-mode remote sensing images to train, verify and test a multi-classifier, a high-rise feature fusion algorithm being used for making a training set, a verification set and a test set of the classifier, constructingthe classifier model parameters for classifying newly generated data sets. Remarkable effect can be achieved in the remote sensing image ground feature classification through deep learning. The methodis suitable for classification of complex and marine remote sensing images and has the characteristics of being precise, rapid, efficient and safe, so that the utilization rate and the utilization value of the remote sensing images are improved.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to the field of deep learning, and in particular to a method for fusion and classification of high-level features of multi-modal remote sensing images based on deep learning. Background technique [0002] With the development of remote sensing technology, it is of great research value to efficiently apply remote sensing technology to regional land surveys, quantitatively extract land use information, and monitor and evaluate the environment. Among them, remote sensing image classification technology, as an important support for remote sensing applications, has been widely used in thematic information extraction, change detection, thematic map production and the establishment of remote sensing databases. [0003] Remote sensing image classification is a key technology in remote sensing application systems. Its main task is to interpret and identify the attributes and distribution of g...

AI Technical Summary

Problems solved by technology

When dealing with multimodal remote sensing images from multiple sensors, different resolutions, and different physical representations, although machine learning has been widely used in remote sensing image classification, there are still four problems in this type of method: (1) The choice of multiple modalities is limited; (2) The spectrum, dimension, time phase, resolution and other characteristics of remote sensing images make data processing a severe challenge; (3) How to establish the interaction between multiple modalities It is difficult to improve the classification accuracy of remote sensing images; (4) With the development of remote sensing technology, massive remote sensing image processing is facing challenges

[0010] However, there is no method that can effectively overcome the four problems in the application of machine learning to remote sensing image classification as mentioned above, and can quickly, efficiently and safely classify complex and massive remote sensing data.

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