Fusion of High-Resolution SAR and Optical Imageries Based on a Wavelet Transform and IHS Integrated Algorithm

Yehia, Abdelrahman; Safy, Mohamed; Amein, Ahmed S.

doi:10.4028/www.scientific.net/jera.52.62

Cited by 9 publications

(7 citation statements)

References 23 publications

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“…Different fusion outputs were compared using different image quality indices. The visual and statistical results of the fusion output indicate that this method has effective conversion from SAR to optical images, enhancing the interpretability of SAR images [12]. Dinklage P et al proposed a sequential and parallel algorithm for constructing wavelet trees, which refined the characters represented in nodes of tree in the opposite way as depth increases.…”

Section: Related Workmentioning

confidence: 99%

Construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms

2024

PLoS ONE

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In power electronics systems, system design and operation often involve multiple time and space scales, ranging from nanosecond switching dynamics to hour-level system operation behavior. Due to the complexity of these systems and the rise of wide-gap semiconductor technology, a series of multi-scale phenomena have emerged that are difficult to ignore. The high frequency of switching operations makes multi-scale effects particularly significant, including the fast dynamic response of the power loop, EMI, and heat conduction problems. They are key factors that must be considered in the design to ensure the efficient and reliable operation of power electronic devices. This study proposes the construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms to address the multi-scale phenomenon and limitations of single-scale power electronic converters. Firstly, a joint scale model for power electronic converters at both macro and micro-scales was established, targeting both single-scale models and simple combinations of multiple scale models for power electronic converters. The traditional single-scale model is sufficient to describe the average behavior of the converter, but it has serious limitations in capturing fast transient processes and high-frequency switching behavior in power electronic systems. These limitations often manifest themselves when there is a need to capture fine timescales of detail. By transforming between the time domain and the frequency domain, wavelet decomposition enables the model to capture both macroscopic average characteristics and microscopic transient dynamics. The wavelet reconstruction algorithm can simulate all kinds of fast changes in the actual working process more accurately and compress irrelevant information while retaining key signal features, so as to optimize the simulation performance of the model. Secondly, this algorithm is used to analyze BC in short time scale. Finally, the short time scale characteristics of power electronic converters are analyzed. Experimental results show that the fusion of wavelet decomposition and reconstruction algorithm enhances the accuracy of the power electronic converter model and improves the performance of the system. The model achieves an error reduction of nearly 3% in the calculation step size of 10-7s, which has a significant impact on the high precision requirements of high-frequency operations. In addition, the optimal calculation step size of 8×10-8s achieves an error reduction of more than 14%, making an important contribution to the transient analysis and fine structure simulation. The wavelet algorithm can improve the accuracy of multi-scale modeling in power electronic system and reduce the simulation time. The reduction of error not only shows the improvement of the accuracy of the model, but also shows its practical significance in the design and test of the actual power electronic system. The reduction in error reveals the ability to more accurately predict and mitigate potential performance problems in matching tests with actual hardware, as well as its ability to adapt to emerging wide bandgap semiconductor materials and structures.

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Section: Related Workmentioning

confidence: 99%

Construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms

2024

PLoS ONE

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“…Wavelet analysis has been applied in the field of radio [72] (such as noise reduction for acoustic or video signals), meteorological [73][74][75] and hydrological information analysis [76,77]., image enhancement [78] and image fusion [79]. Hyperspectral images have a very high spectral resolution with hundreds of bands forming an approximately continuous spectral curve [80].…”

Section: Wavelet Analysismentioning

confidence: 99%

Assessment of Poplar Looper (Apocheima cinerarius Erschoff) Infestation on Euphrates (Populus euphratica) Using Time-Series MODIS NDVI Data Based on the Wavelet Transform and Discriminant Analysis

et al. 2021

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Poplar looper (Apocheima cinerarius Erschoff) is a destructive insect infesting Euphrates or desert poplars (Populus euphratica) in Xinjiang, China. Since the late 1950s, it has been plaguing desert poplars in the Tarim Basin in Xinjiang and caused widespread damages. This paper presents an approach to the detection of poplar looper infestations on desert poplars and the assessment of the severity of the infestations using time-series MODIS NDVI data via the wavelet transform and discriminant analysis, using the middle and lower reaches of the Yerqiang River as a case study. We first applied the wavelet transform to the NDVI time series data in the period of 2009–2014 for the study area, which decomposed the data into a representation that shows detailed NDVI changes and trends as a function of time. This representation captures both intra- and inter-annual changes in the data, some of which characterise transient events. The decomposed components were then used to filter out details of the changes to create a smoothed NDVI time series that represent the phenology of healthy desert poplars. Next the subset of the original NDVI time series spanning the time period when the pest was active was extracted and added to the smoothed time series to generate a blended time series. The wavelet transform was applied again to decompose the blended time series to enhance and identify the changes in the data that may represent the signals of the pest infestations. Based on the amplitude of the enhanced pest infestation signals, a predictive model was developed via discriminant analysis to detect the pest infestation and assess its severity. The predictive model achieved a severity classification accuracy of 91.7% and 94.37% accuracy in detecting the time of the outbreak. The methodology presented in this paper provides a fast, precise, and practical method for monitoring pest outbreak in dense desert poplar forests, which can be used to support the surveillance and control of poplar looper infestations on desert poplars. It is of great significance to the conservation of the desert ecological environment.

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“…Currently, MST-based algorithms are the most widely studied and applied among these fusion algorithms, including wavelet transform [15], Laplacian pyramid transform [16], non-subsampled shear wave transform [17], and non-subsampled contourlet transform [18]. The typical procedure followed by these algorithms involves decomposing the source images into multiple frequency scales, and subsequently devising distinct fusion strategies for each of these parts.…”

Section: Introductionmentioning

confidence: 99%

MGFuse: An Infrared and Visible Image Fusion Algorithm Based on Multiscale Decomposition Optimization and Gradient-Weighted Local Energy

Zhang

Wang

2023

IEEE Access

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Existing image fusion algorithms have difficulty in effectively preserving valuable target features in infrared and visible images, which easily introduces blurry edges and unremarkable notable targets during their fusion process. We propose the MGFuse algorithm as a solution to this problem, which is a novel fusion algorithm that utilizes multiscale decomposition optimization and gradient-weighted local energy. Initially, non-subsampled shearlet transform (NSST) is applied to partition both the infrared and visible images into several high-frequencies and low-frequencies components. Subsequently, the acquired low frequencies continue to be decomposed via proposed optimization function to get base layers and texture layers, which can optimize the quality of image edges and preserve fine-grained details, respectively. In addition, we have formulated an intrinsic attribute-based energy (IAE) fusion scheme to merge the two base layers. The texture layers and high-frequencies are extracted by gradient-weighted local energy (GE) operator based on structure tensor, which are employed to construct the fusion strategy for these parts. At last, the acquired texture and base parts are linearly combined to get the integrated low-frequency layer on which the final image is acquired using inverse NSST. Numerous experimental observations demonstrate that our MGFuse algorithm achieve superior fusion capability than the reference nine advanced algorithms in both qualitative and quantitative assessment, and robustness to noisy images with different noise levels.

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Fusion of High-Resolution SAR and Optical Imageries Based on a Wavelet Transform and IHS Integrated Algorithm

Cited by 9 publications

References 23 publications

Construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms

Construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms

Assessment of Poplar Looper (Apocheima cinerarius Erschoff) Infestation on Euphrates (Populus euphratica) Using Time-Series MODIS NDVI Data Based on the Wavelet Transform and Discriminant Analysis

MGFuse: An Infrared and Visible Image Fusion Algorithm Based on Multiscale Decomposition Optimization and Gradient-Weighted Local Energy

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