Remote sensing image scene classification method and device

Abstract

The invention provides a remote sensing image scene classification method and a device thereof. The method comprises: extracting picture feature factors of input remote sensing image pictures based onthe compressed convolutional neural network, and performing scene classification on the pictures using a clustering support vector machine based on the picture feature factors. The remote sensing image scene classification method and device provided by the invention reduces model parameters of the convolutional neural network by compressing the convolutional neural network, obviously reduces theoccupied storage space in the case of not reducing the scene classification accuracy, and speeds up the classification and improves the classification efficiency by using a strategy of clustering before classifying.

Description

technical field [0001] The present invention relates to the technical field of information processing, and more specifically, to a remote sensing image scene classification method and device. Background technique [0002] With the development of my country's aerospace technology, more and more high-resolution satellites are launched into space to obtain data on the earth's surface. The high-resolution satellites launched into space can play a role in disaster monitoring, agricultural production estimation, and military investigation. Usually, the data transmitted from high-resolution satellites to the ground has a huge frame size. In order to make full use of these large and large high-resolution remote sensing data, scene classification is a very important preprocessing method. It is necessary for further target recognition, change detection, and image retrieval. It is of great help for other tasks. [0003] At present, there are mainly the following methods for remote sen...

AI Technical Summary

Problems solved by technology

However, for remote sensing images, the classification accuracy is still not up to the expected goal, because most of the models currently used for this task are trained on natural image datasets, and the pertinence for remote sensing images is not strong. And because there are few feature parameters in the data set of remote sensing images, while the convolutional neural network has many parameters, large models, and high computational consumption, it is easy to cause overfitting of remote sensing images, so it is difficult to store and store them on mobile devices. well applied to practical tasks

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