Optimal texture image reconstruction method for improvement of SAR image matching

Ghannadi, Mohammad Amin; Saadatseresht, M.; Izadi, Moein; Alebooye, Saeedeh

doi:10.1049/iet-rsn.2020.0058

Cited by 10 publications

(8 citation statements)

References 33 publications

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“…Then, using Multi-Looking and Goldstein Filters, apply various filters to the interference diagram to aid in the accurate completion of the unwrapping process [5]. The snaphu is the mean statistical-cost network-flow algorithm for phase unwrapping, and it is an independently licensed utility that performs phase unwrapping outside of the Sentinel Application Platform [15]. The unwrapped phase is then imported into the Sentinel Application Platform, where it is converted to metrical units using an external reference digital elevation b a 1064 (2022) 012015 IOP Publishing doi:10.1088/1755-1315/1064/1/012015 4 model [16], and geocoded using the terrain correction [1].…”

Section: Data Acquisition and Methodsmentioning

confidence: 99%

Terrain Mapping for the Southwestern Desert of Iraq Using Interferometry Method from Sentinel-1A Images

Aryan¹,

Rasib²

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Synthetic aperture radar Interferometry is a popular three-dimensional imaging technique for creating a Digital Elevation Model. Using traditional methods for creating DEMs and terrain mapping is one of the methods that require high cost and time-consuming, which has affected the creation and updating of terrain maps in Iraq, so this study aims to use the InSAR technology to generate DEM, which contributes to the creation of terrain maps. In this work, the synthetic aperture radar interferometry approach was used on the interference stack generated from a pair of Sentinel-1A images within the SNAP program to generate a DEM and a terrain map of the desert region in south-western Iraq. The elevations of the digital elevation model were compared with those of the RTK-GCPs points in the region of interest. The results obtained from this study are a terrain map with the contour lines generated from the digital elevation model created by the InSAR technique with an accuracy of 18 m, with the root mean square error of the DEM being 8.17. The outputs prove the effectiveness of InSAR technology in generating accurate DEM that contributes to creating terrain maps in less time and cost than traditional methods.

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Section: Data Acquisition and Methodsmentioning

confidence: 99%

Terrain Mapping for the Southwestern Desert of Iraq Using Interferometry Method from Sentinel-1A Images

Aryan¹,

Rasib²

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Kushwaha and Patel proposed a weighted median filter; this method has a good denoising effect on impulse noise and small speckle noise, for example, ceramic surface image, but it is not ideal for denoising the image with a large scatter size like ceramic surface image [10]. Ghannadi et al [11] proposed a new full variational block matching ceramic surface image noise reduction algorithm by combining the full variational algorithm with the 3D block matching image noise reduction algorithm [12]. Chen et al proposed an image correlation noise reduction algorithm, which first uses the target characteristics to reject the stationary target and then further filters the random noise by using the persistence of target motion and the spatial location correlation of target echoes in consecutive multiframe images [13].…”

Section: Related Workmentioning

confidence: 99%

“…Ceramic Surface Image Texture Feature Extraction. The wavelet transform has better local and multiresolution analysis capability to approximate and detect edges at multiple scales, suppressing noise at large scales and pinpointing edges at small scales, and smoothing the noise while still preserving the image edges [11]. Define the two-dimensional smoothing function, use its first-order partial derivatives in the horizontal and vertical directions as the two fundamental wavelets of the image transform, and then define the convolution of the scaling wavelets of the fundamental wavelets in the two directions with the image as the horizontal and vertical components of the wavelet transform, respectively, and find out the modulus and amplitude angle of the wavelet transform, and define the modulus maximum of the wavelet transform along the phase angle direction as the image edge.…”

Section: Wavelet Analysis-based Texture Analysis Of Ceramic Surface Imagesmentioning

confidence: 99%

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Wavelet Analysis-Based Texture Analysis of Ceramic Surface Images

Liu

Zhou

Yang

2021

Advances in Mathematical Physics

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This paper is conducted to explore a new characterization method as a supplement to the traditional roughness characterization. The main research includes the extraction and evaluation of damage features of ceramic surface morphology by applying wavelet methods, the extraction of damage features in surface contours by using wavelet analysis, and the quantitative evaluation of damage degree by using damage rate and damage mean spacing. By comparing various fractal dimension calculation methods, a fractal dimension method suitable for calculating the ceramic surface was selected, and the fractal method was used to describe the ceramic surface topography as a whole. By comparing different methods of calculating the fractal dimension and further verifying them with the measured three-dimensional morphology, it is found that the vibrational method is more suitable for calculating the fractal dimension of ceramic surface, and its calculation accuracy is investigated, and the results show that the method is a reliable one. Based on the fractal theory, a mathematical model of surface wear and surface sealing was established. Further study of the model shows that the surface with a large fractal dimension has a good sealing effect; the surface corresponding to the best fractal dimension is the most resistant to wear. The fractal method can characterize the complexity of the surface profile as a whole. The wavelet method can describe the ceramic surface profile from a local perspective, and the combination of the two methods can characterize the ceramic surface well. Finally, the experimental device of the ceramic surface defect detection system is constructed, and the joint debugging of hardware and software is completed. Under different light source intensities, ceramic image samples are collected, and the accuracy detection experiments of sample defective edges are conducted, and the results show that the light source has a small impact on the accuracy of ceramic defective edge detection. The results show that the light source has more influence on the accuracy of scratch detection. The results show that the system constructed in this thesis has good applicability for different ceramic sample detection.

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“…The footprint of many of the remote-sensing sensors cover large areas with high temporal resolution; thus, they can potentially capture the spatial and temporal variabilities of HABs, as evidenced by the extensive literature describing the detection, monitoring, and forecasting of HABs using remote sensing-based techniques and sensors [9]. Investigations utilizing moderate-resolution imaging spectroradiometers (MODIS-Aqua and MODIS-Terra), SeaWiFS, MERIS, Sentinel-2, and unmanned aerial vehicles have contributed the most to these studies [9,[37][38][39][40][41][42]. The more recent and advanced satellites (e.g., Sentinel-3, launched in February 2016) provide added valuable resources for ocean color products, yet their recent deployment and, hence, their short record of historical data compared to earlier operational satellites (e.g., MODIS: 1999-present) puts them on the waiting list for future machine learning-based forecasting projects.…”

Section: Introductionmentioning

confidence: 99%

A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom

et al. 2021

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In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena in Florida’s coastal areas. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, we developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models the K. brevis abundance is used as the target, and 10 level-02 ocean color products extracted from daily archival MODIS satellite data are used as controlling factors. The adopted approach addresses two main shortcomings of earlier models: (1) the paucity of satellite data due to cloudy scenes and (2) the lag time between the period at which a variable reaches its highest correlation with the target and the time the bloom occurs. Eleven spatio-temporal models were generated, each from 3 consecutive day satellite datasets, with a forecasting span from 1 to 11 days. The 3-day models addressed the potential variations in lag time for some of the temporal variables. One or more of the generated 11 models could be used to predict HAB occurrences depending on availability of the cloud-free consecutive days. Findings indicate that XGBoost outperformed the other methods, and the forecasting models of 5–9 days achieved the best results. The most reliable model can forecast eight days ahead of time with balanced overall accuracy, Kappa coefficient, F-Score, and AUC of 96%, 0.93, 0.97, and 0.98 respectively. The euphotic depth, sea surface temperature, and chlorophyll-a are always among the most significant controlling factors. The proposed models could potentially be used to develop an “early warning system” for HABs in southwest Florida.

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Optimal texture image reconstruction method for improvement of SAR image matching

Cited by 10 publications

References 33 publications

Terrain Mapping for the Southwestern Desert of Iraq Using Interferometry Method from Sentinel-1A Images

Terrain Mapping for the Southwestern Desert of Iraq Using Interferometry Method from Sentinel-1A Images

Wavelet Analysis-Based Texture Analysis of Ceramic Surface Images

A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom

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