Image Fusion in Remote Sensing Applications: A Review

Pandit, Vaibhav R.; Bhiwani, R. J.

doi:10.5120/21263-3846

Cited by 71 publications

(51 citation statements)

References 50 publications

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“…A large spectrum of image-based classification problems (Malamas, Petrakis, Zervakis, Petit, & Legat, 2003;Pandit & Bhiwani, 2015;Ranjidha, Kumar, & Saranya, 2013) are usually approached through the abstraction of highly spatially correlated images (like the ones considered in this article) into vectors of distinctive visual features. Such a practice permits to eliminate redundancy, improve reliability, and reduce the classification algorithm's training time.…”

Section: Image Feature Vectorsmentioning

confidence: 99%

An inference model for combustion diagnostics in an experimental oil furnace

et al. 2017

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The continuous monitoring of the air/fuel ratio, oil/water/air temperatures, and gas/particulate emissions of combustion processes in oil‐based furnaces allows experts to detect anomalies and act to prevent faults and critical conditions. These important but tedious tasks can be performed by an expert system designed to mimic the human abilities of recognizing relevant patterns and finding their most likely causes. In this article, we present the architecture of an expert system that uses flame images grabbed during the combustion process in an experimental oil furnace as input parameters. Computational processing of those images provides feature vectors for analysis by “artificial experts” that correlate changes in flame appearance with typical combustion states. The Dempster–Shafer method is used to build the knowledge base and the inference engine. The results of tests in which flame conditions are suddenly modified by altering the physical variables of the combustion process revealed that the method can properly combine measures from various flame image characteristics to issue diagnostics. Such diagnostics are similar to those given by human experts. This suggests that the proposed approach may fill the gap between models based on features extracted from images and real‐world operating conditions. This is the intended contribution of this work.

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Section: Image Feature Vectorsmentioning

confidence: 99%

An inference model for combustion diagnostics in an experimental oil furnace

et al. 2017

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“…3. Root Mean Squared Error (RMSE): The RMSE measures the difference between the reference and the fused image [24]. It is defined as…”

Section: Spectral Angle Mapper (Sam): the Spectral Anglementioning

confidence: 99%

“…The aim is to obtain an image of higher quality than the individual images. Image Fusion is a subset of the more diverse research area data fusion [7]. It provides a framework and tools that align data originating from different sources with the aim of obtaining information of greater quality depending on the type of application [8].…”

Section: Introductionmentioning

confidence: 99%

Oil spill segmentation in fused Synthetic Aperture Radar images

Longman

Mihaylova

Coca

2016

2016 4th International Conference on Control Engineering &Amp; Information Technology (CEIT)

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Abstract-Synthetic Aperture Radar (SAR) satellite systems are very efficient in oil spill monitoring due to their capability to operate under all weather conditions. Systems such as the Envisat and RADARSAT have been used independently in many studies to detect oil spill. This paper presents an automatic feature based image registration and fusion algorithm for oil spill monitoring using SAR images. A range of metrics are used to evaluate the performance of the algorithm and to demonstrate the benefits of fusing SAR images of different modalities. The proposed framework has shown 45% improvement of the oil spill location when compared with the individual images before the fusion.

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“…The fusion of the panchromatic and MS image known as pansharpening has been the subject of numerous publications in the literature (Joshi et al, 2016;Pandit and Bhiwani, 2015). The proposed methods can be classified into methods with injection of high spatial frequencies, methods based on substitution of components and methods based on a multiresolution analysis.…”

Section: Introductionmentioning

confidence: 99%

Application of Morphological Component Analysis to Optical Image Fusion

Loum¹,

Armand²,

Ghislain³

et al. 2017

American Journal of Applied Sciences

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Abstract:The image fusion technique is widely used in remote sensing. Its purpose is to provide comprehensive information without arte facts by combining the partial information from different source images. In this study, we propose a new model of images fusion with very high spatial resolution. We use the separation capacities of the Morphological Component Analysis (MCA) to extract the smooth and texture components of our images. These morphological components are then fused separately using the decomposition in the Laplacian pyramids for the smooth part and bivariate Hahn polynomials for texture part. Finally the image fusion is obtained through linear combination of merged smooth and texture components. The experiments carried out on IKONOS, LANDSAT and Quick Bird remote sensing images show the good performances of our method which has been compared to conventional methods. The performances obtained in our experiments are characterized by a small global metric such as ERGAS equals to 3.88 for IKONOS image and 3.65 for QuickBird image compared to 8.70 for IKONOS image and 6.97 for QuickBird for conventional HIS algorithms. We also have a mean loss of 15% for spectral information com pare to those of the conventional methods which revolve around 25%. The degradation of spatial information in order of 17% in contrast to conventional HIS algorithms which oscillate around 21%.

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Image Fusion in Remote Sensing Applications: A Review

Cited by 71 publications

References 50 publications

An inference model for combustion diagnostics in an experimental oil furnace

An inference model for combustion diagnostics in an experimental oil furnace

Oil spill segmentation in fused Synthetic Aperture Radar images

Application of Morphological Component Analysis to Optical Image Fusion

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