Urban land utilization information analysis method based on deep neural network

Abstract

The invention discloses an urban land utilization information analysis method based on a deep neural network. The method comprises the steps of by means of an artificial ground control point selectingmethod, registering pre-processed fully polarized SAR data and an optical image, thereby respectively extracting a polarizing characteristic and an optical characteristic; inputting the polarizing characteristic and the optical characteristic into a pre-trained SAE classifier, thereby obtaining a first primary classification result which corresponds with the optical characteristic, and a second primary classification result that corresponds with the polarizing characteristic; wherein the SAE classifier is obtained through training an architecture based on the deep neural network by means of amarked sample; and performing decision grade fusion on the first primary classification result and the second primary classification result according to a D-S evidence theory, thereby obtaining urbanland utilization classification information. The method according to the invention settles a problem of limited urban city utilization extracting precision by means of a single sensor in prior art.

Description

technical field [0001] The invention belongs to remote sensing image processing technology, in particular to a method for analyzing urban land use information based on a deep neural network. Background technique [0002] Urbanization is one of the concentrated manifestations of intense human activities changing nature, and urban land use change is one of the most important responses to the urbanization process. Land use classification reflects the forms of human land use and transformation, and reflects the inherent characteristics of land use itself. Urban land use change is a comprehensive reflection of the interaction between long-term human activities and the natural environment. It is closely related to human economic and social development, and is also closely related to natural processes such as runoff process, evaporation process, ecological process, and urban heat island effect. Urban land use change brings complex ecological and environmental consequences, affecti...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the problems in the prior art, the present invention provides a method for analyzing urban land use information based on a deep neural network, which solves the problem of limited extraction accuracy of urban land use using a single sensor in the prior art

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