And realizing building instance mask extraction method of remote sensing image based on U-shaped CNN model

A technology of remote sensing images and extraction methods, applied in neural learning methods, biological neural network models, character and pattern recognition, etc., can solve problems such as difficult masks and inability to extract masks, and achieve improved accuracy, excellent performance, and excessive suppression The effect of compensation

Active Publication Date: 2019-08-09
CHINA UNIV OF GEOSCIENCES (WUHAN)
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Problems solved by technology

It remains an important and challenging task due to the following issues, on the one hand, due to the inability of current semantic segmentation methods to efficiently extract individual building masks from c...

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  • And realizing building instance mask extraction method of remote sensing image based on U-shaped CNN model
  • And realizing building instance mask extraction method of remote sensing image based on U-shaped CNN model
  • And realizing building instance mask extraction method of remote sensing image based on U-shaped CNN model

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[0032] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific implementation manners of the present invention will now be described in detail with reference to the accompanying drawings.

[0033] Please refer to figure 1 with figure 2 , which are respectively the high-resolution remote sensing image building extraction flow chart of the embodiment of the present invention and the model architecture diagram of the embodiment of the present invention, specifically including the following steps:

[0034] S1. The multi-scale fusion U-shaped network preprocesses the input remote sensing image data; the preprocessing includes 2 times upsampling, original scale and 2 times downsampling preprocessing operations, through which the input remote sensing image data is processed It is processed as three parallel input streams, namely: 2X input stream, 1X input stream, and 0.5X input stream; wherein, the 2X input str...

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Abstract

The invention provides a building mask extraction method for realizing a remote sensing image based on a multi-scale and multi-task U-shaped CNN network model. Accurate building mask extraction of a high-resolution remote sensing image under different scales is realized in an instance segmentation mode. According to the building mask extraction method provided by the invention, a multi-scale multi-task network integrating a U-shaped network, a region suggestion attention network and edge constraint loss is applied, and the whole network is quickly converged by optimizing multi-task mixed lossof a joint ECL and the like, so that excessive compensation in limited available training data is inhibited. According to the method, excellent performance and high robustness are achieved on different scales, and the precision of various data sets is remarkably improved.

Description

technical field [0001] The present invention relates to the technical field of building instance extraction from high-resolution remote sensing images, and more specifically, to a method for extracting building instance masks from remote sensing images based on a multi-scale and multi-task U-shaped CNN model. Background technique [0002] Abstract buildings extracted from high-resolution remote sensing images have been widely used for automatic surveying and mapping through deep learning. It remains an important and challenging task due to the following issues, on the one hand, due to the inability of current semantic segmentation methods to efficiently extract individual building masks from cadastral databases with variable scales; on the other hand, Existing fully convolutional networks struggle to obtain accurate masks using limited available training sets when the convolutional layers become deeper. Contents of the invention [0003] The technical problem to be solved...

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Application Information

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IPC IPC(8): G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06V10/462G06N3/045
Inventor 刘袁缘方芳郭明强覃杰陈鼎元杨淞晰
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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