Inverse proportional surface fitting-based image registration method for interferometric synthetic aperture sonar

Huang, Pan; Tang, Jinsong; Wang, Jinbo; Zhong, Heping; Yue, Jun; Guo, Mengqi

doi:10.1109/coa.2016.7535631

Cited by 5 publications

(6 citation statements)

References 3 publications

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“…The results of mean filter with 5×5 window size, Lee filter, regularized P‐M filter and the proposed method are shown in Figure 2c–f respectively. In order to compare the results quantitatively, residue number [18], edge preserve index (EPI) [17], and time cost are selected, which are shown in Table 1. From Table 1, it can be seen that the residue number of the proposed method is the least in the four methods.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Adaptive filtering method combined with quality map for InSAS interferogram

Huang

Teng

Zhong

2022

Electronics Letters

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The quality of interferogram, which is limited by various phase noise, will greatly influence the subsequent processes of interferometric synthetic aperture sonar (InSAS), such as phase unwrapping and digital elevation model (DEM) estimation. Therefore, phase filtering is an essential step in signal processing for InSAS. In this paper, an adaptive filtering method combined with quality map is proposed to suppress the phase noise while preserving the fringe edges. The main advantage of the new approach is the size of filtering window can be adaptively selected according to the quality map. A function between window size and quality map is established, which controls the strength of filtering. In the area of high quality, the window size is large to remove noise, otherwise, in the low quality area, the window size is small to keep edge details. The effectiveness of the proposed method is validated via some illustrative simulated experiment and the real InSAS data.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Adaptive filtering method combined with quality map for InSAS interferogram

Huang

Teng

Zhong

2022

Electronics Letters

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“…This method uses the atmospheric compensation factors retrieved from the statistics of the image scene and is suitable for regions where solar illumination intensity is unknown (Agrawal et al 2011). The Hyperion image were geometrically co-registered to OLI image using well-distributed 57 control points based on the popular polynomial transformation method (Huang et al 2016;Phetcharat et al 2014;Song et al 2017;Tanács et al 2015). The accuracy was controlled by calculating the RMS error (RMSE = 0.39).…”

Section: Pre-processingmentioning

confidence: 99%

Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks

2020

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The deactivation of Earth Observing-1 satellite has resulted in the termination of capturing Hyperion data as a unique source of hyperspectral satellite imagery. These images also were collected through an on-demand service and thereby are not available for the entire Earth's surface. The Operational Land Imager (OLI) sensor, on the other hand, provides a free source of multi-spectral images with global coverage. Recognized these facts, the aim of this paper is to enhance the spectral resolution of OLI images by using existing Hyperion imageries to generate a high spectral resolution image for a desired date and site. This was conducted through the artificial neural network (ANN). To find the suitable ANN, we compared the performance of multilayer perceptron (MLP) and radial basis function (RBF) networks for spectral enhancement.The research obtained two Hyperion and OLI images covering West Region of Tehran, Iran, on 4 January 2016. From 242 original Hyperion spectral bands, we selected 31 bands to reproduce from OLI spectral bands. These were determined through visual inspection, principal component analysis and Pearson's correlation test. The MLP and RBF networks were generated based on the OLI bands 1-7 and per 31 Hyperion bands as input and output layers respectively. The comparison between the spectral bands of spectra-enhanced image and original Hyperion data indicated a good agreement (0.884 > R 2 > 0.692). This study also found MLP network delivered higher accuracy against RBF network for spectral enhancement. The spectra-enhanced image can be used in studies with the need of images with continuous spectral bands.

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“…The InSAS workflow primarily consists of synthetic aperture imaging, 24–29 complex image registration, 30,31 interferometric map generation, unflattening, phase filtering, phase unwrapping, 32,33 and digital elevation model reconstruction. The interferometric phase map obtained after complex image registration and conjugate multiplication is generally not used directly for phase unwrapping because the interferogram SNR is low at this time, there is a lot of noise in the image, and the phase in the interferogram cannot reflect the real topography.…”

Section: Introductionmentioning

confidence: 99%

“…20,21 Its primary military applications include detection of mines and unexploded ordnance and reconnaissance of important sea areas, while its main civil applications include salvaging wrecks and sunken objects, 22 mapping underwater topographies, surveying submarine deposits, and finding submarine fiber optic cables. 23 The InSAS workflow primarily consists of synthetic aperture imaging, [24][25][26][27][28][29] complex image registration, 30,31 interferometric map generation, unflattening, phase filtering, phase unwrapping, 32,33 and digital elevation model reconstruction. The interferometric phase map obtained after complex image registration and conjugate multiplication is generally not used directly for phase unwrapping because the interferogram SNR is low at this time, there is a lot of noise in the image, and the phase in the interferogram cannot reflect the real topography.…”

Section: Introductionmentioning

confidence: 99%

Review of interferometric synthetic aperture sonar interferometric phase filtering methods

Huang,

Ding,

Wang

et al. 2023

Advances in Mechanical Engineering

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Interferometric synthetic aperture sonar (InSAS) is a novel three dimensional underwater mapping system, which is widely used in various fields. Interferometric phase filtering is a crucial process in InSAS signal processing. The study of interferogram filtering method has great significance to improve the signal-to-noise ratio (SNR) of interferogram, reduce the difficulty of phase unwrapping, and improve the accuracy of digital elevation maps. Firstly, this article introduces the sources of interferometric phase noise. Secondly, it illustrates the difference between real domain filter and complex domain filter by simulation experiment. Thirdly, the various methods of InSAS interferogram filtering are systematically studied, which can be classified into spatial domain filtering and transform domain filtering. The transform domain filtering can be further divided into frequency domain and wavelet transform filtering. Moreover, the advantages and disadvantages of various methods are analyzed theoretically. Finally, numerical simulation and real-data processing experiments are performed, and the advantages and shortcomings of several methods are compared using residue numbers, root mean square error (RMSE), and CPU time as evaluation criteria.

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Inverse proportional surface fitting-based image registration method for interferometric synthetic aperture sonar

Cited by 5 publications

References 3 publications

Adaptive filtering method combined with quality map for InSAS interferogram

Adaptive filtering method combined with quality map for InSAS interferogram

Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks

Review of interferometric synthetic aperture sonar interferometric phase filtering methods

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