Pixel level fusion techniques for SAR and optical images: A review

Kulkarni, Samadhan C.; Rege, Priti P.

doi:10.1016/j.inffus.2020.01.003

Cited by 200 publications

(75 citation statements)

References 101 publications

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“…If fdesc 1 ( i ) and fdesc 2 ( j ) are matched, record their location, respectively. If the locations are also the same, it means that both of the content and the location of the region that computed the SIFT descriptors are the same [ 20 ]. Finally, the SIFT descriptors that meet the above conditions are recorded to generate a matching degree map match_map, where 1 < = i < = n .…”

Section: Methodsmentioning

confidence: 99%

“…For example, literature [ 16 ] proposes a novel image fusion algorithm based on deep support value convolutional neural network, literature [ 17 ] proposes the medical image fusion with the all convolutional neural network, and literature [ 18 ] proposes a general image fusion framework based on convolutional neural network, which is called IF-CNN. Literatures [ 19 , 20 ] review the recent advances and future prospects about deep learning for pixel-level image fusion. In the above methods, the good results are obtained for their better learning ability than the traditional neural network models.…”

Section: Introductionmentioning

confidence: 99%

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A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

Wang

Chang

Liu

et al. 2021

Journal of Healthcare Engineering

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The traditional medical image fusion methods, such as the famous multi-scale decomposition-based methods, usually suffer from the bad sparse representations of the salient features and the low ability of the fusion rules to transfer the captured feature information. In order to deal with this problem, a medical image fusion method based on the scale invariant feature transformation (SIFT) descriptor and the deep convolutional neural network (CNN) in the shift-invariant shearlet transform (SIST) domain is proposed. Firstly, the images to be fused are decomposed into the high-pass and the low-pass coefficients. Then, the fusion of the high-pass components is implemented under the rule based on the pre-trained CNN model, which mainly consists of four steps: feature detection, initial segmentation, consistency verification, and the final fusion; the fusion of the low-pass subbands is based on the matching degree computed by the SIFT descriptor to capture the features of the low frequency components. Finally, the fusion results are obtained by inversion of the SIST. Taking the typical standard deviation, QAB/F, entropy, and mutual information as the objective measurements, the experimental results demonstrate that the detailed information without artifacts and distortions can be well preserved by the proposed method, and better quantitative performance can be also obtained.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

Wang

Chang

Liu

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

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“…It is pointed that: two identically structured objects may appear different in optical imagery due to their spectral responses, which can be identical in SAR imagery [85]. Therefore, SAR and optical imagery can offer complementary information to each other and the fusion of these images can generate an image with both rich spatial structure and spectral informat Similar to literature survey [85] , many traditional SARoptical image fusion in pixel level can also be classified into four categories namely component substitution methods (CS), multiscale decomposition methods (MSD), hybrid methods, and learning-based methods. Component substitution methods and multiscale decomposition methods almost can also be applied to any fusion scenario such as Pan-sharpening, spatialtemporal or infrared-visible light image fusion.…”

Section: A Sar and Optical Image Fusionmentioning

confidence: 99%

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

Liu¹

2021

Preprint

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In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

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“…Many fusion methods and techniques have been implemented to improve and develop the image merging process to reach the best results. Various recent surveys outline these methods [2][3][4][5]. Many transforms are used in the fusion field, like Stationary Wavelet Transform, Discrete Wavelet Transform, Curvelet Transform, etc.…”

Section: Introductionmentioning

confidence: 99%

Image Focus Enhancement Using Focusing Filter and DT-CWT Based Image Fusion

Habeeb

2021

eijs

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Combining multi-model images of the same scene that have different focus distances can produce clearer and sharper images with a larger depth of field. Most available image fusion algorithms are superior in results. However, they did not take into account the focus of the image. In this paper a fusion method is proposed to increase the focus of the fused image and to achieve highest quality image using the suggested focusing filter and Dual Tree-Complex Wavelet Transform. The focusing filter consist of a combination of two filters, which are Wiener filter and a sharpening filter. This filter is used before the fusion operation using Dual Tree-Complex Wavelet Transform. The common fusion rules, which are the average-fusion rule and maximum-fusion rule, were used to obtain the fused image. In the experiment, using the focus operators, the performance of the proposed fusion algorithm was compared with the existing algorithms. The results showed that the proposed method is better than these fusion methods in terms of the focus and quality.

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Pixel level fusion techniques for SAR and optical images: A review

Cited by 200 publications

References 101 publications

A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

Image Focus Enhancement Using Focusing Filter and DT-CWT Based Image Fusion

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