input data： TIF files, each TIF file has dimensions of A×B, with a total of 25 files in this paper.

Execution Steps: 1.Contrastive network.py 2.Organize the data to obtain the model parameters with the closest and farthest distances. 3.output image of Contrastive_network.py 4.3guiyihua.py(Max-min normalization) 5.AUCHE.Py（Average the normalization results representing the closest and farthest） 6.Pu-baggingDT.py 7.Precision_evaluation.py（Average operation: Results from Pu-baggingDT and AUCHE.py，and precision evaluation）