Random Collective Representation-Based Detector with Multiple Features for Hyperspectral Images
Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD). However, the sliding dual window of the original CRD introduces high computational complexity. Moreover, most HAD mod...
Main Authors: | Zhongheng Li, Fang He, Haojie Hu, Fei Wang, Weizhong Yu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-02-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/4/721 |
Similar Items
-
CRNN: Collaborative Representation Neural Networks for Hyperspectral Anomaly Detection
by: Yuxiao Duan, et al.
Published: (2023-06-01) -
Pairwise Elastic Net Representation-Based Classification for Hyperspectral Image Classification
by: Hao Li, et al.
Published: (2021-07-01) -
Selective Search Collaborative Representation for Hyperspectral Anomaly Detection
by: Chensong Yin, et al.
Published: (2022-11-01) -
A weighted multiple-feature fusion classifier for hyperspectral images with limited training samples
by: Jinghui Yang, et al.
Published: (2018-01-01) -
Adaptive window based collaborative representation for hyperspectral anomaly detection with fusion of local and global information
by: Maryam Imani
Published: (2023-08-01)