Nov 6, 2023 · An enhanced tensor low-rank representation (ETLR) learning model is proposed for HAD. Specifically, the original 3-D HSI data is first decomposed into a ...

An enhanced tensor low-rank representation (ETLR) learning model is proposed for HAD, and a robust dictionary tensor that can adequately characterize the ...

Oct 22, 2024 · In this letter, an enhanced tensor low-rank representation learning (ETLR) model is proposed for HAD. Specifically, the original 3-D ...

Nov 2, 2023 · This approach separates HSI into a sparse anomaly component and a low-rank back- ground component, and then uses the GRX [6] algorithm to detect ...

The main objective of the present paper is to propose a design of an efficient intrusion detection model with high accuracy, better time efficiency, and reduced ...

Nov 6, 2023 · Anomaly detection has been known to be a challenging problem due to the uncertainty of anomaly and the interference of noise. In this paper, we ...

The enhanced tensor low-rank representation (ETLR) [45] divides the HSIs into background, anomaly, and noise components while preserving the intrinsic ...

1 day ago · It simplifies the background model using a Gaussian multivariate distribution, allowing for the detection of anomaly targets that deviate from ...

A novel low-rank representation with dual graph regularization and an adaptive dictionary (DGRAD-LRR) is proposed for hyperspectral anomaly detection.

Sep 13, 2024 · This paper proposes a weighted low-rank sparse dictionary learning method (WLSDL). This model organically combines sparse representation with low-rank ...