A review of remote sensing image fusion methods

By Hassan Ghassemian. Information Fusion, 2016

780 citations recorded by the source corpus.

How researchers see this paper

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

A review of multimodal image matching: Methods and applications (2021, background): “Image fusion is one of the most important applications of image registration technology [13,478].”
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, neutral): “, 2015) Giving a comprehensive discussion on data fusion problems in RS by analyzing the Data Fusion Contests (Ghassemian, 2016) Introducing the RS fusion methods in pixel/feature/decision-level and different evaluation criteria (Schmitt and Zhu, 2016) Modeling the data fusion process, along with in”
Visible and Infrared Image Fusion Using Deep Learning (2023, background): “multi-exposure image fusion (MEF) [124], medical image fusion (MEDIF) [125], and remote sensing image fusion [126].”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “There exists a wide range of approaches to data fusion; see, for example, [11]–[13].”
LMSeg: Language-guided Multi-dataset Segmentation (2023, background): “Image Segmentation has been a longstanding challenge in computer vision and plays a pivotal role in a wide variety of applications ranging from autonomous driving (Levinson et al., 2011; Maurer et al., 2016) to remote sensing image analysis (Ghassemian, 2016).”