Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation

By Yang Xu, Zenbin Wu, Jun Yu Li, Antonio J. Plaza, Zhihui Wei. IEEE Transactions on Geoscience and Remote Sensing, 2016

520 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/29580626129d00992feac5dbbccbe5a538b3caa8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Antonio J. Plaza
2. Gong Cheng
3. Lina Zhuang
4. Ceyuan Yang
5. Junwei Han
6. José M. Bioucas-Dias

In Computer Vision and Pattern Recognition

1. Gong Cheng
2. Junwei Han
3. Xiwen Yao
4. Lei Guo
5. Peicheng Zhou
6. Antonio J. Plaza
7. Ceyuan Yang
8. Lianru Gao

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiwen Yao
4. Lei Guo
5. Peicheng Zhou
6. Antonio J. Plaza
7. Ceyuan Yang
8. Lina Zhuang

Citations in the corpus

• BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection (2023, methodology): “4) San Diego Dataset: It was also acquired from the AVIRIS covering the San Diego airport scene [38], and three aircraft of larger size are considered to be anomalies in this dataset.”
• When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs (2018, methodology): “Based on the features used for scene classification, the existing approaches can be roughly categorized into three main categories [1]: handcrafted featurebased methods [16]–[24], unsupervised feature learning-based methods [25]–[36], and deep feature learning-based methods [7]–[12], [37]–[43].”