Hyperspectral Target Detection : An Overview of Current and Future Challenges

By Nasser M. Nasrabadi. IEEE Signal Processing Magazine, 2014

550 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Analytical Chemistry, Media Technology

View Paper: https://www.semanticscholar.org/paper/b9845df54aa4b73771634eed1b75fc442243bd67

How researchers see this paper

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

In Aerospace Engineering

1. Luís Pádua
2. Qian Du
3. Raul Morais
4. Jonás Hruska
5. José Elievam Bessa
6. Weiwei Sun
7. Telmo Adão
8. Emanuel Peres

In Analytical Chemistry

1. Qian Du
2. Weiwei Sun
3. Wei Li

In Media Technology

1. Qian Du
2. Wei Li
3. Weiwei Sun
4. Luís Pádua
5. Raul Morais
6. Jonás Hruska
7. José Elievam Bessa
8. Guodong Wu

Citations in the corpus

• Hypersectral Imaging for Military and Security Applications: Combining Myriad Processing and Sensing Techniques (2019, background): “For military and defense applications, the current automatic target recognition capabilities are extended to situations in which the targets are smaller, more diverse, more fleeting, and less distinctive, while the background clutter is more complex [19].”
• Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, background): “Essentially, target/anomaly detection consists in a binary classification technique that labels every pixel in the hyperspectral cube as belonging to target/background or outlier/background, respectively [70].”
• The explanation game: a formal framework for interpretable machine learning (2020, unknown): “ML has been used to help evaluate loan applications (Khandani, Kim, & Lo, 2010) and student admissions (Kotsiantis, 2012), predict criminal recidivism (Dressel & Farid, 2018), and identify military targets (Nasrabadi, 2014), to name just a few controversial examples.”
• Hyperspectral Band Selection: A Review (2019, background): “, biophysical parameter retrieval [3], [4], fine classification of complex environments [5], [6], small target detection [7], [8], and so forth.”
• Hyperspectral Image Classification Using Deep Pixel-Pair Features (2017, background): “Taking advantage of the rich spectral information, classification using hyperspectral data has been developed for a variety of applications [1]–[6], such as landuse land-cover mapping, mineral exploration, water pollution detection, etc.”