Remote sensing image semi-automatic labeling method and device based on deep learning

A technology of remote sensing imagery and deep learning, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve the problem of high cost of image labeling and achieve the effect of reducing subjective assumptions and improving efficiency

Active Publication Date: 2021-07-13
COMP NETWORK INFORMATION CENT CHINESE ACADEMY OF SCI +1
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Problems solved by technology

[0006] Aiming at the problem of high cost of image annotation acquisition in the process of remote sensing image information extraction, the present invention, based on the idea of ​​uncertainty sampling, proposes a semi-automatic remote sensing image annotation method that uses a combination of deep neural network probability density function and superpixel segmentation

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  • Remote sensing image semi-automatic labeling method and device based on deep learning
  • Remote sensing image semi-automatic labeling method and device based on deep learning
  • Remote sensing image semi-automatic labeling method and device based on deep learning

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Embodiment Construction

[0031] The present invention will be further described below through specific implementation examples and accompanying drawings.

[0032] 1) Semantic segmentation network model training

[0033] The semantic segmentation network is a fully convolutional neural network, and the entire fully convolutional neural network represents a differentiable score function: from the original image pixel at one end to the class score at the other end, for each pixel, usually the output is along each pixel position. Each class score vector s(Scores) arranged along the depth dimension, the scores (probabilities) provide a very reliable measure of uncertainty for semantic recognition.

[0034] The neural network is optimized using the cross-entropy loss function, that is, the cross-entropy between the ground truth label t and the output s of the neural network is calculated, where the ground truth label (ground truth) t is a positive class and c-1 One-hot encoded vectors of negative classes (...

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Abstract

The invention relates to a remote sensing image semi-automatic labeling method and device based on deep learning. The method comprises the following steps: pre-training a full convolutional neural network by using a cross entropy loss function based on a public remote sensing data set; predicting a to-be-labeled remote sensing image by adopting a pre-trained full convolutional neural network, and outputting a category attribute probability; according to the category attribute probability, calculating an uncertainty metric value of a remote sensing image pixel, and setting a threshold value to extract an uncertain pixel; according to the minimum percentage of uncertain pixels in the superpixels segmented by the remote sensing image, screening the superpixels as recommended labeling areas, and carrying out manual labeling on the recommended labeling areas; and combining the manual labelling of the recommended labelling area and the full convolutional neural network prediction result of the remaining area to obtain a final labelling result. A manual labelling person can be free from burdens of heavy manual drawing of accurate boundaries, the manual annotation efficiency is improved, and the labelling workload and subjective assume of manual labelling are reduced.

Description

technical field [0001] The invention belongs to a remote sensing image tagging method in the field of remote sensing images, relates to efficient semi-automatic tagging and recommended tagging strategies, and is mainly used in remote sensing image information extraction, pixel-level tagging data acquisition and other applications. Background technique [0002] In recent years, the technology of automated remote sensing image information extraction has made great progress. Due to the diversity of remote sensing images, a single machine learning model cannot cover them all. That is, a deep learning model that performs well on some datasets may perform poorly on other datasets. For example, farmland has completely different image representations in different regions, different seasons, and different landforms, so it still faces great challenges. [0003] The information extraction of remote sensing images mainly relies on the color and shape of image targets. In the face of la...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06N3/045G06F18/2415
Inventor 赵江华惠健王学志周园春
Owner COMP NETWORK INFORMATION CENT CHINESE ACADEMY OF SCI
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