Domain-adaptation algorithm for remotely sensing building changes through instance contrast learning
Building-change detection automatically identifies changes in ground buildings in remote-sensing images acquired in the same geographic area at different times.Fully supervised change-detection algorithms require a large amount of labeled remote sensing data to make accurate predictions.Manually labeling a building change detection label is time-consuming and labor intensive because it requires a professional to compare and label two images pixel by pixel.Unsupervised domain adaptation technique is an effective means to alleviate this problem.Although the current-domain adaptation algorithm has achieved good results in building-change detection,the following problems persist:Problem 1:A class-based domain mixing strategy is applicable to a large number of categories.In building-change detection,only positive samples of the category"change"are available.Problem 2:In the current pixel-based contrast learning method,pseudo labels generated by a model must have samples with classification errors because the labels of target domains are unidentifiable.This requirement introduces large noise information during contrast training.Problem 3:The pseudo label generated by high-confidence threshold filtering does not leverage the low confidence prediction results of a teacher model.To solve the above problems,this paper proposes a case-level contrast-learning domain-adaptation algorithm for cross-domain building-change detection task.This paper proposes an instance contrast-learning domain adaptation for change detection(ICDA-CD)method for cross-domain building-change detection.The main contributions are as follows:(1)A region-level domain-mixing method is proposed,which combines data containing the buildings in a source domain and data containing buildings in a target domain on one sample simultaneously.(2)Case-level contrast learning method is proposed.In the encoder,the distance between the biphasic features of a changing building area is pulled apart.In the decoder,the distance between the features of each changing building area is narrowed.(3)A pseudo label quality estimation method is proposed.The pseudo-label quality of each pixel position is estimated by the value predicted by a teacher model,and then loss is weighted.Domain migration experiments were performed on the LEVIR-CD and S2Looking datasets,and comparison and ablation experiments were performed with advanced domain-adaptation algorithms.In the migration of the LEVIR-CD task to the S2Looking task,the proposed algorithm achieved the highest F1 and IOU of 43.91 and 28.31,respectively.In the migration of the S2Looking task to the LEVIR-CD task,the proposed algorithm achieved the highest F1 scores and IOU of 74.75 and 59.68,respectively.To solve the problem of unsupervised domain adaptive change detection algorithm across data domains,an ICDA-CD method was proposed.The accuracy of the cross-domain unsupervised domain adaptive change detection algorithm was effectively improved by using region-level domain mixing,case-level contrast learning,and pseudo label quality estimation-weighted loss.