Hyperspectral Anomaly Detection With Otsu-Based Isolation Forest
Hyperspectral anomaly detection involves in many practical applications. Traditional anomaly detection methods are mainly proposed based on statistical models and geometrical models. This article proposes an Otsu-based isolation forest method, which applies the assumption that anomaly pixels are mor...
Main Authors: | Yuxiang Zhang, Yanni Dong, Ke Wu, Tao Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9531356/ |
Similar Items
-
Ship detention prediction using anomaly detection in port state control: model and explanation
by: Ran Yan, et al.
Published: (2022-08-01) -
Matrix Autoregressive Model for Hyperspectral Anomaly Detection
by: Jingxuan Wang, et al.
Published: (2022-01-01) -
Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
by: Zhangming Xiong, et al.
Published: (2022-10-01) -
Tensor Decomposition-Inspired Convolutional Autoencoders for Hyperspectral Anomaly Detection
by: Bangyong Sun, et al.
Published: (2022-01-01) -
SI2FM: SID Isolation Double Forest Model for Hyperspectral Anomaly Detection
by: Zhenhua Mu, et al.
Published: (2023-01-01)