研究生学术报告预告登记(开题、中期、答辩)

       为加强研究生学术交流活动,推进学术创新,特开通“研究生学术报告预告区”。我校研究生和教师可以在预告区及时发布和了解有关研究生学术报告的信息,届时参加。也可就某学术报告展开专题讨论与交流。

报告人: 郭建华
学号: 1017234040
学院: 电气与自动化工程学院
报告类型: 其他学术报告
日期: 7 April 2021
时间: 7:00 PM
地点: 26教学楼534会议室
导师: 杨敬钰
题目: Unsupervised Domain-Invariant Feature Learning for Cloud Detection of Remote Sensing Images
内容提要:

In recent years, several advanced convolutional neural net-work (CNN) models have been proposed that can successfully detect clouds in multispectral satellite images. These networks include classical super-pixel classification, typical end-to-end CNN-based frameworks, and multi-scale/level feature fusion networks. However, such networks rely heavily on large number of samples annotated at pixel-level for parameter tuning, and sample collection is tedious, time-consuming, and expensive. To reduce the labeling cost, we propose an unsupervised domain adaptation (UDA) approach to enable the model trained on labeled source satellite images to generalize to unlabeled target satellite images. Specifically, we propose a fine-grained feature alignment (FGFA) domain adaptation strategy that encourages a cloud detection network to extract domain-invariant representations, which improves the accuracy of cloud detection in unlabeled target satellite images. The proposed FGFA strategy consists of two steps: i) class-relevant feature selection based on an attention-guided mechanism and ii) class-relevant feature alignment based on a proposed grouped feature alignment approach. Experimental results on the “Landsat-8 → ZY-3” and “GF-1→ ZY-3” domain adaptation tasks demonstrate the effectiveness of our method and its superiority to existing state-of-the-art UDA approaches.

图片:
登记人: 郭建华
登记时间: Thursday, 15 April 2021, 5:09 PM