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

A remote sensing image and depth convolution technology, which is applied in the field of remote sensing image target detection, can solve the problems of multiple misclassifications, missing classifications, and low classification accuracy, and achieve fast and accurate detection, improved detection accuracy, and high efficiency.

Pending Publication Date: 2020-12-01
CHONGQING GEOMATICS & REMOTE SENSING CENT
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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 ...

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

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Embodiment Construction

[0034] The specific implementation manner and working principle of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0035] Such as figure 1 As shown, a remote sensing image blue-roof detection method based on large-scale deep convolutional neural network, the specific steps are as follows:

[0036] Step 1: Obtain the training data set and label it, specifically:

[0037] a) Using Google Earth as the main data source to collect remote sensing image data of the Blue Roof;

[0038] b) Since the resolution of the remote sensing image is relatively high, if it is directly fed into the network, there will be too many parameters, so the remote sensing image is first cropped;

[0039] c) Use the labelme labeling tool to label the image, and the labeling format is unified into the COCO format.

[0040] Step 2: Construct a network model including a feature extraction network, a context enhancement module, a target area g...

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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...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V20/176G06N3/045
Inventor 李朋龙丁忆马泽忠敖影钱进朱智勤李鹏华肖禾陈静刘建欧其健陈培恩陈甲全李政杨光谱
Owner CHONGQING GEOMATICS & REMOTE SENSING CENT
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