A support vector method for anomaly detection in hyperspectral imagery

By Amit Banerjee, Philippe M. Burlina, C. Diehl. IEEE Transactions on Geoscience and Remote Sensing, 2006

400 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/163092692ed4497ba81ed8fea973ed2b5bba4923

How researchers see this paper

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

In Atmospheric Science

1. Shutao Li
2. Jun Yu Li
3. Xudong Kang
4. Jon Atli Benediktsson
5. Antonio J. Plaza
6. Kenli Li
7. Xiangping Zhang
8. Luís Pádua

In Ecology

1. Antonio J. Plaza
2. Jon Atli Benediktsson
3. Shutao Li
4. Xudong Kang
5. Jun Yu Li
6. Luís Pádua
7. Raul Morais
8. Jonás Hruska

In Media Technology

1. Shutao Li
2. Xudong Kang
3. Jun Yu Li
4. Jon Atli Benediktsson
5. Antonio J. Plaza
6. Leyuan Fang
7. Lianru Gao
8. José M. Bioucas-Dias

Citations in the corpus

• Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, methodology): “Anomaly detection, which is used to recognize patterns standing out from cluttered backgrounds, can be addressed with approaches based on Reed-Xiaoli (RX), support vector data description (SVDD) and anomalous change detection (ACD).”
• BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection (2023, methodology): “Besides, diverse techniques were applied to HAD, such as SVDD using the kernel trick avoids a priori assumption [16], covariance analysis with similar specifications has been employed to extract both spatial and spectral characteristics of HSIs [17], [18], [19], and Parzen Windowing-based detector…”
• Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation (2016, methodology): “Moreover, kernel-based methods such as kernel-RX [10], [11] and support vector data description [12], [13] were proposed based on the kernel theory for extending the original space to a higher dimensional feature space.”
• Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters (2017, methodology): “By contrast, the LRX, SVDD, and CRD methods are all quite time consuming, and thus, hard to be used in real applications.”
• Outlier Detection (2008, background): “In this sense, depth functions can measure the “extremeness” or “outlyingness” of a data point with respect to a given data set.”
• Kernel Methods in Computer Vision (2009, methodology): “They propose a method based on support vector data description with a Gaussian kernel [Banerjee et al., 2006].”