Unmanned aerial vehicle remote sensing image shadow removing method based on deep learning

Abstract

The invention discloses an unmanned aerial vehicle remote sensing image shadow removing method based on deep learning, and the method comprises the steps: firstly carrying out the data collection through an unmanned aerial vehicle, carrying out the radiation normalization and geometric registration of the data, and constructing an unmanned aerial vehicle shadow database; then, on the basis of theshadow database, the conditional generative adversarial network 1 is used for learning the shadow removal relation between the sample pairs, and therefore preliminary shadow removal is achieved; considering the radiation difference before and after shadow removal, constructing a non-shadow region radiation normalization database, and training a conditional generative adversarial network 2 on the basis of the database; and finally, performing radiation normalization processing on the shadow removal preliminary result based on the relationship to obtain a final shadow removal result. According to the method, the flexibility of unmanned aerial vehicle data acquisition is considered, the shadow image data set is acquired and constructed, and the transformation relationship between the sample pairs is deeply mined by using the deep learning theory so as to obtain the optimal shadow removal result. The method is high in accuracy, high in calculation efficiency, easy to implement, high in expandability and high in practical value.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and relates to a method for removing shadows, in particular to a method for removing shadows from UAV remote sensing images based on deep learning. Background technique [0002] Shadows widely exist in high-resolution remote sensing images, especially in urban areas with dense buildings, causing brightness loss of local information and directly affecting the accuracy of remote sensing interpretation. Therefore, in order to improve the utilization efficiency of remote sensing images, it is very necessary to remove shadows in high-resolution remote sensing images. [0003] Existing methods can be mainly divided into two categories: local matching method and global optimization method. The local matching method is better for the case of a single type of object inside the shadow, but because it is more sensitive to sample selection, and for the case of complex objects inside ...

AI Technical Summary

Problems solved by technology

The local matching method is better for the case of a single type of object inside the shadow, but because it is more sensitive to sample selection, and for the case of complex objects inside the shadow, the artifacts in the compensation results are obvious, and it is easy to produce serious color loss. Partial

The global optimization method obtains the global optimal solution through iterative optimization, which can often obtain better overall correction results, but for complex shadows covering multiple surface types, it often leads to color distortion and blurred details

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