A deep pixel-level guided neural network based on human-computer interaction for remote sensing data re-labeling
This paper proposes a pixel level human-machine interactive deep neural network(DGN)method for remote sensing image annotation products,which can automatically generate image annotations and allow annotators to adaptively correct previous annotations through simple guidance information after discovering errors.A new training method and measurement standard are adopted to measure the efficiency of re-annotation.The algorithm was experimentally validated using different infrastructure and backbone networks on the Vaihingen dataset,and the results showed that DGN can effectively guide the guidance module to utilize guidance information,increasing the efficiency of re labeling by 2.52 times and improving the accuracy of classification to a certain extent.
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