A multi-scale residual road extraction method for global perception of high-resolution remote sensing images

Abstract

The invention relates to a global perception high-resolution remote sensing image multi-scale residual road extraction method. The proposed global perception high-resolution remote sensing image multi-scale residual learning road extraction method can increase the model to capture long-distance dependence from two aspects. On the one hand, between residual modules, we adopt global perception operations to capture long-distance dependencies from both spatial and channel dimensions; on the other hand, within residual modules, we use multi-scale residual convolution to learn , while extracting multi-scale features, the network receptive field is increased, and more abundant information is provided for the subsequent global perception operation; the invention can solve the mismatch between the limited receptive field of the existing deep learning model and the long-span connection attribute of the road to a certain extent, The problem of scattered road recognition can significantly improve the road recognition accuracy.

Description

technical field [0001] The invention is based on the field of remote sensing image technology processing, and in particular relates to a global perception high-resolution remote sensing image multi-scale residual road extraction method. Background technique [0002] As an important national infrastructure construction, road is an important object in high-resolution remote sensing images, and has a very important impact on human life. For a country, the more developed the transportation system, the more conducive to the country's economic and social construction. The traditional road information acquisition method requires professionals to map, vectorize, and finally acquire road data. This process requires a lot of manual participation, many external factors interfere, and the update speed is slow, which can no longer meet the needs of rapid economic and social development. In the high-resolution remote sensing images, the roads are obviously linearly distributed, the edges...

AI Technical Summary

Problems solved by technology

[0006] The proposed multi-scale residual road extraction method for global sensing high-resolution remote sensing images increases the ability of the model to capture long-distance dependencies from two aspects. Two dimensions to capture long-distance dependencies; on the other hand, in the residual module, we use multi-scale residual convolution learning to extract multi-scale features while increasing the network receptive field, providing more information for the subsequent global perception operation. Abundant information; the present invention can solve the problem of fragmented road recognition caused by the limited receptive field of the existing deep learning model and the mismatch of road long-span connection attributes

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