Abstract
Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.
Highlights
In the late 1970s, with the emergence and development of image sensors, multi-sensor information fusion facilitated the emergence of branch-image fusion, an emerging research field combining sensors, signal processing, image processing, and artificial intelligence while using images as the research object in the field of information fusion
The novelty of the work in this paper can be summarized as follows: 1) this paper summarizes the existing multi-sensor image fusion algorithms, fusion strategies and evaluation indicators relatively completely, which has a reliable reference value for the subsequent image fusion researches; 2) unlike other image fusion reviews, this paper summarizes image fusion algorithms based on the emerging theory-deep learning; 3) coupled with the analysis of the development trends in the field of image fusion, the paper provides a reference for researchers to further explore the research in this direction, which can promote the innovative development of this field
Scholars have proposed a variety of image fusion and objective performance evaluation methods, there are still some problems with these approaches at present
Summary
In the late 1970s, with the emergence and development of image sensors, multi-sensor information fusion facilitated the emergence of branch-image fusion, an emerging research field combining sensors, signal processing, image processing, and artificial intelligence while using images as the research object in the field of information fusion Visible images can provide texture detail, along with high spatial resolution and high definition in a manner consistent with the human visual system, but do not work well in all-day/night conditions. Due to the redundancy and complementarity between the image information obtained by different image sensors (or the same image sensor in different working modes), when compared to any of the individual input remote images, a more comprehensive and accurate image description of a certain scene can be obtained through the fuse of multiple source images (as illustrated in Figure 1) [5]; this approach overcomes the limitations of and differences between the geometric, spectral and spatial resolution of single sensor images, improves image clarity and comprehensibility, and provide more effective information for subsequent image processing task (e.g. image segmentation [6] [7], classification [8], saliency [9] [10], target detection and recognition [11] [12], localization [13], medical diagnosis [14], surveillance [15], energy monitoring [3] [16], agricultural applications [17] and military applications [18])
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