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Received：30 June 2023，
Published：07 July 2024
移动端阅览
建筑物变化检测是遥感影像智能解译中的重要研究方向。针对建筑物变化检测的跨域变化检测算法中存在的像素级对比学习噪声过大和目标域样本利用不充分等问题，本文提出了实例级对比学习域适应变化检测算法ICDA-CD。首先，使用区域级域混合替代实例级域混合，以实现源域和目标域图像的有效混合；然后，利用实例级对比学习，降低伪标签噪声的影响。具体来说，在编码器中，拉开变化实例区域双时相特征距离，并在解码器中，拉近各个变化实例特征之间的距离，这可以显著提升模型对源域和目标域特征表示的一致性；最后，在损失计算部分使用伪标签质量估计，使得低置信度区域的像素也可以参与训练，提高了目标域样本的利用率。将本文方法与DACS、DAFormer、和HRDA等3种目前先进的算法进行实验对比。结果表明本文所提出来的方法在LEVIR-CD域迁移至S2Looking以及S2Looking域迁移至LEVIR-CD时F1分数分别达到了43.91%和74.75%，优于几种先进的算法。
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.
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