A remote sensing image semi-automatic labeling method based on deep learning

A remote sensing image, semi-automatic technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problems of low training efficiency and slow image annotation, and achieve the effect of improving accuracy and improving image annotation efficiency.

Pending Publication Date: 2019-04-23
BEIJING AEROSPACE TITAN TECH CO LTD
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AI Technical Summary

Problems solved by technology

[0005] To sum up, the conventional prediction method is to label a large number of data sets, spend a long time training the

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  • A remote sensing image semi-automatic labeling method based on deep learning
  • A remote sensing image semi-automatic labeling method based on deep learning
  • A remote sensing image semi-automatic labeling method based on deep learning

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Embodiment Construction

[0032] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0033] Such as figure 1 As shown, the present invention proposes a semi-automatic labeling method for remote sensing images based on deep learning. Firstly, the labeling and training are interleaved, and after labeling the area of ​​interest (the area of ​​interest should contain all the features of the target to be predicted , according to the number of target features in the specified area. For example, to predict a house in a community, you need to mark one house by one color, one high-level mark one, and one low-level mark one, etc.), the model immediately participates in training and learns this part The features of the image, and then use these updated features to make predictions, which can be quickly annotated to other images. Add the predicted image features after regularization (regularization is the processing of the prediction re...

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Abstract

The invention provides a semi-automatic remote sensing image annotation method based on deep learning, and the method comprises the steps of 1) carrying out the manual annotation of a target in a region of interest of a remote sensing image, adding the annotated image into a sample library, and training a prediction model; 2) carrying out target labeling on the original image of the selected areaby utilizing the prediction model to obtain a target area of the prediction image, carrying out regularization processing on the target area, adding the regularized prediction image into a sample library, and training the prediction model; 3) continuously repeating the step 2) to optimize the target annotation and the prediction model training at the same time to obtain an optimal prediction model, and 4) using the optimal prediction model to perform target annotation on the remote sensing image. According to the method, the annotation and the learning are carried out at the same time, the fewer manual intervention is needed along with increase of learning characteristics, and the annotation efficiency is greatly improved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence image recognition, in particular to a semi-automatic tagging method for remote sensing images based on deep learning. Background technique [0002] Image annotation is the most cumbersome part of artificial intelligence image processing, and it requires a lot of manpower, material and financial resources for pure manual annotation. The images processed by common annotation software are relatively small, and none of them are aimed at remote sensing image processing. The amount of remote sensing image data is relatively large, and the commonly used image labeling method needs to divide the large image and label it one by one, which is extremely inefficient. It is cumbersome to use remote sensing professional software for vector labeling. It needs to be vectorized first, then assign values ​​to the label elements, and finally convert the vector data to raster data. [0003] Automati...

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Application Information

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IPC IPC(8): G06F16/51G06F16/583G06T7/11G06T7/136G06T7/187G06N3/04
CPCG06T7/11G06T7/136G06T7/187G06T2207/20081G06T2207/10032G06N3/045
Inventor 刘志强
Owner BEIJING AEROSPACE TITAN TECH CO LTD
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