A feature-based metric for the quantitative evaluation of pixel-level image fusion

Liu, Zheng; Forsyth, David; Laganière, Robert

doi:10.1016/j.cviu.2007.04.003

Cited by 69 publications

(25 citation statements)

References 9 publications

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“…In the evaluation of multiple indicators, each fusion method had its own advantages and disadvantages. Among them, the CC shows the degree of change of the image before and after the fusion which is crucial to the accuracy of subsequent treatment [41]. In order to preserve the characteristics of the original image to the maximum extent, we choose the G-S transform with the highest CC as the fusion method of this paper.…”

Section: Multispectral and Panchromatic Image Fusion Results Based Onmentioning

confidence: 99%

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

et al. 2018

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Abstract:To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP) is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram-Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT), and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper.

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Section: Multispectral and Panchromatic Image Fusion Results Based Onmentioning

confidence: 99%

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

et al. 2018

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“…Entropy (NCIE) [52] B. Image Feature Based Metrics Gradient-based fusion Metric (G) [53] Multiscale Image Fusion Metric (M) [54] Spatial Frequency Image Fusion Metric (SF) [55] (we also list as Zheng) Phase Congruency Image Fusion Metric (P) [56,57] (not tested in this paper)…”

Section: (A) Information-theory Based Metricsmentioning

confidence: 99%

LANDSAT satellite image fusion metric assessment

Blasch

Liu

2011

Proceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON)

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Image fusion is an important component of many applications such as inspection, night vision, medical diagnosis, and electro-optical (multispectral) targeting. In this paper, we build upon our previous results for image fusion metrics by applying the methods to satellite imagery versus night vision (visual and infrared imagery) and single-metric biomedical image fusion analysis. Multispectral examples include satellite imagery for land-cover analysis. In this paper, we explore a variety of metrics that have been used to quantify the performance of the fused image products. The exhaustive conclusive analysis over all possibilities is not realizable, so in this paper we group the various metrics into categories and demonstrate an application of the metrics to satellite LANDSAT imagery using an entropy-based image fusion method. From the many metrics analyzed, we found the Image Structural Similarity Based (ISSB) metrics are most useful.

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“…Thus, the main purpose of this study is to evaluate different fusion methods, while preserving the spectral information provided in the MIs and examine the classification accuracy for different fusion methods. Also, we apply noise-insensitivity approaches for standardizing and automating the evaluation process, based on different assessment methods described by several authors (Gonzalez et al, 2004;Shi et al, 2005;Acerbi et al, 2006;Petrovic, 2007;Nencini et al, 2007;Karathanassi et al, 2007;Liu et al, 2008;Li et al, 2010;Ehler et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data

Yuhendra

Alimuddin

Sumantyo

et al. 2012

International Journal of Applied Earth Observation and Geoinfor

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A feature-based metric for the quantitative evaluation of pixel-level image fusion

Cited by 69 publications

References 9 publications

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

LANDSAT satellite image fusion metric assessment

Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data

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