Hyperspectral Anomaly Detection Based on Background Purification via Deep Autoencoding Gaussian Mixture Model - researchr publication related

researchr

You are not signed in
Sign in
Sign up

Zhiyue Wang, Junping Zhang, Ye Zhang 0008, Xinyu Zhou. Hyperspectral Anomaly Detection Based on Background Purification via Deep Autoencoding Gaussian Mixture Model. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, Pasadena, CA, USA, July 16-21, 2023. pages 7423-7426, IEEE, 2023. [doi]

The following publications are possibly variants of this publication:

Network Anomaly Detection Using Federated Deep Autoencoding Gaussian Mixture ModelYang Chen, Junzhe Zhang, Chai Kiat Yeo. mln 2020: 1-14 [doi]

Hyperspectral Anomaly Detection via Spatial Density Background PurificationBing Tu, Nanying Li, Zhuolang Liao, Xianfeng Ou, Guoyun Zhang. remotesensing, 11(22):2618, 2019. [doi]

Hyperspectral Anomaly Detection via Integration of Feature Extraction and Background PurificationYong Ma 0001, Ganghui Fan, Qiwen Jin, Jun Huang 0008, Xiaoguang Mei, Jiayi Ma 0001. lgrs, 18(8):1436-1440, 2021. [doi]

Hyperspectral Anomaly Detection via Background Purification and Spatial Difference EnhancementXiaoyi Wang, Liguo Wang, Jiawen Wang, Kaipeng Sun, Qunming Wang. lgrs, 19:1-5, 2022. [doi]

A Background-Purification-Based Framework for Anomaly Target Detection in Hyperspectral ImageryYan Zhang, Yanguo Fan, Mingming Xu. lgrs, 17(7):1238-1242, 2020. [doi]

A Kernel Background Purification Based Anomaly Target Detection Algorithm for Hyperspectral ImageryYan Zhang, Mingming Xu, Yanguo Fan, Yuxiang Zhang, Yanni Dong. igarss 2019: 441-444 [doi]

Log-based Anomaly Detection Without Log ParsingVan-Hoang Le, Hongyu Zhang. 2021:

runs on WebDSL