期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 11, 页码 11385-11396出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3079247
关键词
Feature extraction; Hyperspectral imaging; Estimation; Object detection; Manifolds; Detectors; Anomaly detection; Density estimation; end-to-end (E2E) learning; hyperspectral anomaly detection (HAD); manifold learning; multidistance measure
类别
资金
- National Natural Science Foundation of China [61801359, 62071360]
- Fundamental Research Funds for the Central Universities [20109205456]
- Innovation Fund of Xidian University [20109205456]
The study proposes a novel end-to-end local invariant autoencoding density estimation model to address two issues in hyperspectral anomaly target detection: failure to dig out customized features and incapability of preserving inherent information in low-dimensional representation. By introducing a local invariant autoencoder and multidistance measure, more effective anomaly target detection is achieved.
Hyperspectral anomaly target detection (also known as hyperspectral anomaly detection (HAD)] is a technique aiming to identify samples with atypical spectra. Although some density estimation-based methods have been developed, they may suffer from two issues: 1) separated two-stage optimization with inconsistent objective functions makes the representation learning model fail to dig out characterization customized for HAD and 2) incapability of learning a low-dimensional representation that preserves the inherent information from the original high-dimensional spectral space. To address these problems, we propose a novel end-to-end local invariant autoencoding density estimation (E2E-LIADE) model. To satisfy the assumption on the manifold, the E2E-LIADE introduces a local invariant autoencoder (LIA) to capture the intrinsic low-dimensional manifold embedded in the original space. Augmented low-dimensional representation (ALDR) can be generated by concatenating the local invariant constrained by a graph regularizer and the reconstruction error. In particular, an end-to-end (E2E) multidistance measure, including mean-squared error (MSE) and orthogonal projection divergence (OPD), is imposed on the LIA with respect to hyperspectral data. More important, E2E-LIADE simultaneously optimizes the ALDR of the LIA and a density estimation network in an E2E manner to avoid the model being trapped in a local optimum, resulting in an energy map in which each pixel represents a negative log likelihood for the spectrum. Finally, a postprocessing procedure is conducted on the energy map to suppress the background. The experimental results demonstrate that compared to the state of the art, the proposed E2E-LIADE offers more satisfactory performance.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据