Hyperspectral Anomaly Detection based on Autoencoder using Superpixel Manifold Constraint-Reference-Cited by-同舟云学术

Hyperspectral Anomaly Detection based on Autoencoder using Superpixel Manifold Constraint

Published:2022-09-23 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
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Author:

Gan Yuquan¹^ORCID,Li Wenqiang¹^ORCID,Liu Ying¹^ORCID,He Jinglu¹^ORCID,Zhang Ji²^ORCID

Affiliation:

1. School of Telecommunication and Information Engineering,Center for Image and Information Processing,Xi'an University of Posts and Telecommunications, China

2. University of Southern Queensland,, Australia

Funder

Natural Science Foundation of Shaanxi Province

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3573942.3574108

Reference26 articles.

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2. Yuan, Yuan, Dandan Ma and Qi Wang. 2015. Hyperspectral anomaly detection by graph pixel selection. IEEE transactions on cybernetics 46.12 (2015): 3123-3134.

3. Xiaoqiang Lu, Wuxia Zhang and Xuelong Li. 2017. A hybrid sparsity and distance-based discrimination detector for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing 56.3 (2017): 1704-1717.

4. Telmo Adão, Jonáš Hruška, Luís Pádua, José Bessa, Emanuel Peres, Raul Morais and Joaquim J. Sousa. 2017. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote sensing 9.11 (2017): 1110.

5. Shangzhen Song, Huixin Zhou, Yixin Yang and Jiangluqi Song. 2019 Hyperspectral anomaly detection via convolutional neural network and low rank with density-based clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12.9 (2019): 3637-3649.