Image fusion techniques for remote sensing applications

By Giovanni Simone, Alfonso Farina, Francesco Carlo Morabito, Sebastiano Bruno Serpico, Lorenzo Bruzzone. Information Fusion, 2002

442 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/b7f128a5bbf130ccce9dac9c7bc47e0621877246

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Leyuan Fang
2. Shutao Li
3. Haitao Yin
4. Xudong Kang
5. Jianwen Hu
6. Hassan Ghassemian
7. J. V. van Aardt
8. Fethi B. Ahmed

In Media Technology

1. Leyuan Fang
2. Shutao Li
3. Xudong Kang
4. Haitao Yin
5. Jianwen Hu
6. Jiayi Ma
7. Hassan Ghassemian
8. Hui Li

Citations in the corpus

• Advances in Multi-Sensor Data Fusion: Algorithms and Applications (2009, background): “describe three typical applications of data fusion in remote sensing, such as obtaining elevation maps from synthetic aperture radar (SAR) interferometers, the fusion of multi-sensor and multi-temporal images, and the fusion of multi-frequency, multi-polarization and multi-resolution SAR images [3].”
• Adaptive resonance theory (1997, background): “…{b(nl}, where b(n) is the correct prediction given a(n) An associative learning network and a vigilance controller link these modules to make the ARTMAP system operate in real time, creating the minimal number of ARTa recognition categories, or hidden units, needed to meet accuracy criteria.”
• Pixel-level image fusion: A survey of the state of the art (2017, background): “Major applications In recent years, pixel-level image fusion has been used in a wide variety of applications, such as remote sensing [7, 116], medical diagnosis[10], surveillance[29], and photography[70] applications.”
• A survey of image classification methods and techniques for improving classification performance (2007, unknown): “In recent years, wavelet-merging techniques have shown to be another effective approach to generate a better improvement of spectral and spatial information contents (Li et al. 2002, Simone et al. 2002, Ulfarsson et al. 2003).”
• Assessment of image fusion procedures using entropy, image quality, and multispectral classification (2008, background): “[3] G.”
• A review of remote sensing image fusion methods (2016, methodology): “Some multi-sensor data fusion methods are discussed in [18].”