Remote sensing image blue-topped house detection method based on large-scale deep convolutional neural network

Abstract

The invention discloses a remote sensing image blue-topped house detection method based on a large-scale deep convolutional neural network. The method comprises the steps of obtaining a training dataset and performing labeling; constructing a network model comprising a feature extraction network, a context enhancement module, a target region generation network, a spatial attention module, a pooling layer and a target detection module; inputting the labeled training data set to train a network model; and inputting a to-be-detected remote sensing image into the trained network model to obtain adetection result of the blue-top house. The invention has the advantages that depth feature extraction, target candidate region generation, anchor box generation, context enhancement, a spatial attention mechanism and a target detection process are integrated into an end-to-end deep network model, and a good detection effect can be achieved for multi-scale remote sensing image blue-topped house detection.

Description

technical field [0001] The present invention relates to the technical field of remote sensing image target detection, in particular to the use of a deep neural network model to realize the target detection of remote sensing images with multi-scale changes, and in particular to a blue-roof detection method for remote sensing images based on large-scale deep convolutional neural networks . Background technique [0002] With the development and application of satellite remote sensing technology and computer vision technology, target detection in remote sensing images has become a research hotspot. Using remote sensing images to efficiently and quickly detect blue-roofed houses and other typical high-value targets in the fields of pattern recognition, reconnaissance and detection It has high application value and is also a key research issue in the field of remote sensing intelligent processing. [0003] Traditional pattern recognition and classification methods are widely used...

AI Technical Summary

Problems solved by technology

Due to the limitation of the spatial resolution of the remote sensing image itself and the phenomenon of the same object with different spectra and different objects with the same spectrum, the detection results often have more misclassifications and omissions, resulting in low classification accuracy.

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