Classification method based on high resolution remote sensing image area information and convolution neural network

Abstract

The invention discloses a classification method based on high resolution remote sensing image area information and a convolution neural network. Targeting problems that a quantity of image collection windows is too great and thus the classification efficiency is low when the convolution network is used for remote image classification using a fixed-size window to perform traversing. The invention provides a sampling window determination method based on image area characteristics and improves classification efficiency / The classification method based on high resolution remote sensing image area information and the convolution neural network comprises steps of performing over-segmentation on an image to obtain area information of the image, determining a sampling window according to a certain criterion and sending sampling window data into the convolution neural network to perform classification, wherein a classification result is a classification result of a corresponding area. Targeting the restriction of the prior art in the high resolution remote sensing image, the classification method provided by the invention introduces a convolution neural network model in deep learning to extract image characteristics, provides a new technical scheme to the remote sensing image classification and improves classification accuracy and efficiency.

Description

technical field [0001] The invention belongs to the field of remote sensing image digital image processing, and in particular relates to a classification method based on high-resolution remote sensing image area information and a convolutional neural network. Background technique [0002] High-resolution remote sensing image classification is an important part of remote sensing image understanding and one of the key technologies for remote sensing applications. Image classification is a fundamental task in image understanding. With the development of related technologies, more and more information is contained in remote sensing images, and the description information between object categories is more abundant. It is of great significance to study how to quickly and effectively realize high-resolution image classification. The key of the classification method is to extract the features of different types of ground objects, and realize the classification on the basis of the ...

AI Technical Summary

Problems solved by technology

This processing leads to low computational efficiency, and improper selection of the window size can also significantly affect the classification performance

[0004] From the above, it can be seen that the classification of high-resolution remote sensing images is a challenging task, and the existing technology has great limitations, and it is impossible to quickly and effectively classify high-resolution images.

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