Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images

In order to solve insufficient training data problem in remote sensing, a web platform was created so that registered users can generate labeled data for various classes in a dynamic structure. Users were asked to select representative pixel groups for the forest, hazelnut, shadow, soil, tea, and bu...

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Bibliographic Details
Published in:Geocarto international 2022-09, Vol.37 (18), p.5433-5452
Main Authors: Saralioglu, Ekrem, Gungor, Oguz
Format: Article
Language:English
Subjects:
CNN
crowdsourcing
deep learning
multispectral image classification
Training data
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Summary:In order to solve insufficient training data problem in remote sensing, a web platform was created so that registered users can generate labeled data for various classes in a dynamic structure. Users were asked to select representative pixel groups for the forest, hazelnut, shadow, soil, tea, and building classes with the polygon tool, and then assign a class label corresponding to each created polygon thanks to the help document displaying descriptive information regarding the locations, colors, textures and distributions of the classes in the image. Crowdsourcing was again used to test the accuracy of the tagged data produced by crowdsourcing. The created data set was overlaid with the original WV-2 image, and the correctness of the labels ​​of the polygons was once visually verified. Finally, the WV-2 image, consisting of 40 patches, was classified with CNN and an average of over 95% accuracy was achieved.
ISSN:1010-6049
1752-0762
DOI:10.1080/10106049.2021.1917006