计算机科学
特征(语言学)
人工智能
空格(标点符号)
变更检测
模式识别(心理学)
特征提取
计算机视觉
哲学
语言学
操作系统
作者
Zuowei Zhang,Chuanqi Liu,Fan Hao,Zhunga Liu
标识
DOI:10.1109/cac59555.2023.10450585
摘要
Domain transformation is playing an increasingly important role in heterogeneous change detection. Existent methods, however, cannot guarantee the consistency of reconstructed feature spaces. To solve this issue, we propose an unsupervised change detection method based on the unification of feature space (UFS). First, we construct a convolutional autoencoder based on adaptive instance normalization to unify feature space. Then, we use fuzzy local information c-means to reduce the over-reliance on reconstructed features. Finally, we design a dynamic superpixel-based label assignment (DSLA) rule to increase the number of reliable pseudo-labels used to learn a binary classifier to obtain the CD results. Experimental results on heterogeneous datasets demonstrate the effectiveness of UFS.
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