Super-resolution building fine recognition method based on multi-scale feature deconvolution

Abstract

The invention discloses a super-resolution building fine recognition method based on multi-scale feature deconvolution, comprising the steps of: making a training sample set; constructing an encoder module, a decoder module, a multi-scale feature deconvolution module, a feature The super-resolution semantic segmentation convolutional neural network of the fusion module and the category determination module; input the training sample set to train the super-resolution semantic segmentation convolutional neural network; adopt the trained super-resolution semantic segmentation convolutional neural network to treat The recognition image is processed to obtain the recognition result. Its remarkable effect is: a super-resolution semantic segmentation network MLSRSS-Net based on multi-scale feature deconvolution is proposed, which combines the multi-scale primary feature deconvolution upsampling of the encoder and the advanced object semantic feature deconvolution of the decoder. Upsampling realizes that the input is a remote sensing image with a lower spatial resolution, and the output is a building semantic map with a high spatial resolution, which significantly improves the accuracy of target extraction.

Description

technical field [0001] The invention relates to the technical field of remote sensing image feature extraction and semantic segmentation, in particular to a super-resolution building fine recognition method based on multi-scale feature deconvolution. Background technique [0002] Buildings are one of the important types of ground features. Accurately identifying buildings from remote sensing images can provide important decision support for government departments such as land management and urban planning to carry out land use status investigations and macro-planning. It has important applications in many fields such as the detection of illegal building and occupation of cultivated land, and military reconnaissance. [0003] Recognize buildings from remote sensing images. Depending on the image resolution, the details of the recognition results are also different. For example, high-resolution images at the meter level or sub-meter level can reflect the fine outlines of build...

AI Technical Summary

Problems solved by technology

At present, there are many related technical methods for super-resolution image reconstruction (interpolation and reconstruction from low-resolution images to obtain high-resolution images), but there is a lack of super-resolution semantic segmentation (high-resolution semantic segmentation results obtained from low-resolution images) Technology

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