Image Fusion: Algorithms and Applications

By Tania Stathaki. 2008

420 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/65a3b71be39db1b1e8bbd408cde0af388f3bc07f

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Citations in the corpus

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, background): “Data with different resolutions is a prevalent challenge in multimodal image fusion [13], as well as many other imaging techniques.”
Image Fusion With Convolutional Sparse Representation (2016, background): “2618776 two main categories: spatial domain methods and transform domain methods [2].”
Multi-focus image fusion with a deep convolutional neural network (2017, methodology): “In recent years, various image fusion methods have been proposed, and these methods can be roughly classiﬁed into two categories [1] : transform domain methods and spatial domain methods.”
Satellite remote sensing for applied ecologists: opportunities and challenges (2014, methodology): “New combinations of SRS data with methodologies such as support vector machines (Mountrakis, Im & Ogole 2011) and multisensor image fusion (Stathaki 2008) are constantly tested for application, while investigations for developing new indices to monitor vegetation have been occurring for decades…”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, methodology): “In this context, remote sensing image classification requires methods capable of combining information from multiple sensors with different spatial, spectral, and temporal resolutions to obtain image products with improved overall characteristics [6], [7].”
Simultaneous image fusion and denoising with adaptive sparse representation (2015, background): “Generally, these methods can be grouped into two categories: spatial domain fusion (SDF) and transform domain fusion (TDF) [2].”