Multi-tense remote sensing image land cover classification method based on convolutional neural network

Abstract

The invention discloses a multi-tense remote sensing image land cover classification method based on a convolutional neural network. The method comprises the following steps: step 1, inputting old tense and new tense high-resolution remote sensing image data and carrying out corresponding preprocessing; step 2, constructing a convolutional neural network, including performing feature extraction on the two pieces of tense data input in the step 1, and performing up-sampling on a high-dimensional feature map; step 3, introducing a tense correction algorithm, and performing tense correction by comparing the classification result of the old tense with a reference true value so as to obtain a correction amount used for correcting classification errors; and step 4, performing post-processing on a land cover classification result, and eliminating a plurality of fragment pattern spots generated after tense correction is performed on an output result of the new tense due to incomplete overlapping of prediction pattern spots of the new tense and the old tense, so that land cover classification precision improved. The method can further improve the land cover classification precision.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing and application, in particular to a multi-temporal remote sensing image land cover classification method based on a convolutional neural network. Background technique [0002] Land cover is an important geographic information resource, and it is an indispensable and important basic information for natural resource monitoring, land and space planning, geographic and national conditions survey, and macro-control analysis. In recent years, with the rapid development of 3S technology, the ability to obtain high-resolution satellite image data has been continuously enhanced, and the spectral resolution, spatial resolution, and temporal resolution of satellite imagery have been continuously improved, providing a solid data basis for land cover classification. However, in practical applications, a large amount of image data still relies on manual interpretation and processing, whi...

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Problems solved by technology

[0006] The purpose of the present invention is to solve the problems in the prior art that the accuracy of land cover classification is not high, and to provide a multi-temporal remote sensing image land cover classification method based on convolutional neural network, which can achieve higher land cover classification accuracy requirements

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