Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection
Target detection is an active area in hyperspectral imagery (HSI) processing. Many algorithms have been proposed for the past decades. However, the conventional detectors mainly benefit from the spectral information without fully exploiting the spatial structures of HSI. Besides, they primarily use...
Main Authors: | Xing Wu, Xia Zhang, Nan Wang, Yi Cen |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-01-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | http://www.mdpi.com/2072-4292/11/2/150 |
Similar Items
-
A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended Multi-Attribute Profile
by: Senhao Liu, et al.
Published: (2021-10-01) -
Sparse and Low-Rank Representation With Key Connectivity for Hyperspectral Image Classification
by: Yun Ding, et al.
Published: (2020-01-01) -
Robust Hyperspectral Image Classification by Multi-Layer Spatial-Spectral Sparse Representations
by: Xiaoyong Bian, et al.
Published: (2016-11-01) -
Multi-Task Joint Sparse and Low-Rank Representation for the Scene Classification of High-Resolution Remote Sensing Image
by: Kunlun Qi, et al.
Published: (2016-12-01) -
Low-Rank and Sparse Matrix Decomposition with Cluster Weighting for Hyperspectral Anomaly Detectiond
by: Lingxiao Zhu, et al.
Published: (2018-05-01)