LREN: Low-Rank Embedded Network for Sample-Free Hyperspectral Anomaly Detection

Authors

  • Kai Jiang Xidian University
  • Weiying Xie Xidian University
  • Jie Lei Xidian University
  • Tao Jiang Xidian University
  • Yunsong Li Xidian University

Keywords:

Anomaly/Outlier Detection

Abstract

Hyperspectral anomaly detection (HAD) is a challenging task because it explores the intrinsic structure of complex high-dimensional signals without any samples at training time. Deep neural networks (DNNs) can dig out the underlying distribution of hyperspectral data but are limited by the labeling of large-scale hyperspectral datasets, especially the low spatial resolution of hyperspectral data, which makes labeling more difficult. To tackle this problem while ensuring the detection performance, we present an unsupervised low-rank embedded network (LREN) in this paper. LREN is a joint learning network in which the latent representation is specifically designed for HAD, rather than merely as a feature input for the detector. And it searches the lowest rank representation based on a representative and discriminative dictionary in the deep latent space to estimate the residual efficiently. Considering the physically mixing properties in hyperspectral imaging, we develop a trainable density estimation module based on Gaussian mixture model (GMM) in the deep latent space to construct a dictionary that can better characterize the complex hyperspectral images (HSIs). The closed-form solution of the proposed low-rank learner surpasses existing approaches on four real hyperspectral datasets with different anomalies. We argue that this unified framework paves a novel way to combine feature extraction and anomaly estimation-based methods for HAD, which intends to learn the underlying representation tailored for HAD without the prerequisite of manually labeled data. Code available at https://github.com/xdjiangkai/LREN.

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Published

2021-05-18

How to Cite

Jiang, K., Xie, W., Lei, J., Jiang, T., & Li, Y. (2021). LREN: Low-Rank Embedded Network for Sample-Free Hyperspectral Anomaly Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4139-4146. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16536

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management