Fusion Algorithm OF Optical Images and SAR With SVT and Sparse Representation

Yin, Zhouping

doi:10.21307/ijssis-2017-799

Cited by 14 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hybrid methods combine component substitution methods and multi-scale decomposition methods, such as IHS combined with à Trous wavelet (AWT) [19] and PCA combined with AWT [20], which make full use of the advantages of the two methods. Model-based methods have two types: variational models [21] and sparse representation-based models [22]. Reviews of optical and SAR images fusion can be found in [4,23].…”

Section: Introductionmentioning

confidence: 99%

IHS-GTF: A Fusion Method for Optical and Synthetic Aperture Radar Data

Shao

Guo

2020

Remote Sensing

View full text Add to dashboard Cite

Optical and Synthetic Aperture Radar (SAR) fusion is addressed in this paper. Intensity–Hue–Saturation (IHS) is an easily implemented fusion method and can separate Red–Green–Blue (RGB) images into three independent components; however, using this method directly for optical and SAR images fusion will cause spectral distortion. The Gradient Transfer Fusion (GTF) algorithm is proposed firstly for infrared and gray visible images fusion, which formulates image fusion as an optimization problem and keeps the radiation information and spatial details simultaneously. However, the algorithm assumes that the spatial details only come from one of the source images, which is inconsistent with the actual situation of optical and SAR images fusion. In this paper, a fusion algorithm named IHS-GTF for optical and SAR images is proposed, which combines the advantages of IHS and GTF and considers the spatial details from the both images based on pixel saliency. The proposed method was assessed by visual analysis and ten indices and was further tested by extracting impervious surface (IS) from the fused image with random forest classifier. The results show the good preservation of spatial details and spectral information by our proposed method, and the overall accuracy of IS extraction is 2% higher than that of using optical image alone. The results demonstrate the ability of the proposed method for fusing optical and SAR data effectively to generate useful data.

show abstract

Section: Introductionmentioning

confidence: 99%

IHS-GTF: A Fusion Method for Optical and Synthetic Aperture Radar Data

Shao

Guo

2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…(Ahmed et al 2013) used Vegetation indices (optical),Global Environment Monitoring Index GEMI ,Purified Adjusted Vegetation Index PAVI and polarimetric indices SAR (CPR, HV/HH and HV/VV) to detect the subsurface hotspots. The third part of pixel level is two articles based on Hierarchical Markov Random Fields models (Hedhli et al 2015(Hedhli et al , 2017, and finally nine papers applied others different methods including layer stacking (Sameen et al 2016), Genetic algorithm image fusion technique (Ahmed et al 2016), multi-scale decomposition and sparse representation (Zhouping 2015), the combination method band 3, band 7 of Landsat ETM+ with a modified HH polarization of SAR image (Xiao et al 2014) , Closest Spectral Fit (CSF) algorithm with the synergistic application of multi-spectral satellite images and multi-frequency Synthetic Aperture Radar (SAR) data. (Eckardt et al 2013), applied learning Artificial Neural Network at pixel level ANN (Piscini et al 2017), these three typical manifold learning ; ISOMAP, Local Linear Embedding (LLE), principle component analysis (PCA) and two papers the first are not clear and the last without fusion method.…”

Section: Figure 4: Types Of Combinations Of Satellite Images Used In mentioning

confidence: 99%

Fusing of Optical and Synthetic Aperture Radar (Sar) Remote Sensing Data: A Systematic Literature Review (Slr)

Mahyoub

Fadil

Mansour

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> Remote sensing and image fusion have recognized many important improvements throughout the recent years, especially fusion of optical and synthetic aperture radar (SAR), there are so many published papers that worked on fusing optical and SAR data which used in many application fields in remote sensing such as Land use Mapping and monitoring. The goal of this survey paper is to summarize and synthesize the published articles from 2013 to 2018 which focused on the fusion of Optical and synthetic aperture radar (SAR) remote sensing data in a systematic literature review (SLR), based on the pre-published articles on indexed database related to this subject and outlining the latest techniques as well as the most used methods. In addition this paper highlights the most popular image fusion methods in this blending type. After conducting many researches in the indexed databases by using different key words related to the topic “fusion Optical and SAR in remote sensing”, among 705 articles, chosen 83 articles, which match our inclusion criteria and research questions as results ,all the systematic study ‘ questions have been answered and discussed.</p>

show abstract

“…Paper [13] proposed automated image mosaicking method based on SFM was proposed in, calculating the camera parameters and 3D coordinates of feature points on basis of a set of preprocessing. In paper [14], a fusion algorithm of SAR and optical image with fast sparse representation on low-frequency images was proposed, and a sparse decomposition process is improved to reduce the algorithm running time. Paper [15] proposed an approach based on structure deformation and propagation for achieving the overall consistency in image structure and intensity.…”

Section: Related Workmentioning

confidence: 99%

Three-Step Registration And Multi-Thread Processing Based Image Mosaic For Unmanned Aerial Vehicle Applications

Ding

Wang

2016

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

Abstract-In the area of image mosaic for unmanned aerial vehicle (UAV) applications, the problems of precision and time consumption have drawn many scholars' attention. To address above two problems, a novel algorithm based on three-step registration and multi-thread processing is proposed in this paper. This method divides the image registration into three steps to improve the precision. Firstly, based on the SIFT features, the fast index mechanism k-d tree and the Euclidean distance are utilized to determine the common points between two adjacent images; then, the linear slope constraint model is used to filter the mismatching point-pairs; finally, the RANSAC algorithm is adopted to remove outer points from the common points to ensure matching precision of inter frames. To accommodate the real time requirement of UAV application, a parallel data processing pattern is presented. The multi-core resources and multi-thread computing method of computers are employed adequately in the new pattern to speed up the whole algorithm. Extensive experiments on precision and time consumption show the superior performance of the proposed algorithm.

show abstract

Fusion Algorithm OF Optical Images and SAR With SVT and Sparse Representation

Cited by 14 publications

References 32 publications

IHS-GTF: A Fusion Method for Optical and Synthetic Aperture Radar Data

IHS-GTF: A Fusion Method for Optical and Synthetic Aperture Radar Data

Fusing of Optical and Synthetic Aperture Radar (Sar) Remote Sensing Data: A Systematic Literature Review (Slr)

Three-Step Registration And Multi-Thread Processing Based Image Mosaic For Unmanned Aerial Vehicle Applications

Contact Info

Product

Resources

About