scholarly journals Improved Faraday Rotation Estimation in Spaceborne PolSAR Data Using Total Variation Denoising

2019 ◽  
Vol 11 (24) ◽  
pp. 2943
Author(s):  
Wulong Guo ◽  
Lu Liu ◽  
Bo Liu ◽  
Liang Chen ◽  
Haisheng Zhao ◽  
...  
Keyword(s):  
Total Variation ◽  
Spatial Resolution ◽  
Faraday Rotation ◽  
Noise Suppression ◽  
High Spatial Resolution ◽  
Window Size ◽  
Real Data ◽  
Denoising Method ◽  
Estimation System ◽  
Ionospheric Sounding

Faraday rotation (FR) is a serious problem for spaceborne polarization SAR (PolSAR) systems at L and P bands. One way to solve the problem is to estimate the FR from PolSAR data for further compensation. Therefore, precise estimation of FR from PolSAR data not only determines the compensation effect of polarimetric systems but also benefits the ionospheric sounding with high spatial resolution. Among the factors that affect the FR estimation, system noise is a non-neglectable factor. Although average filtering (AF) has been widely used in previous works for noise removing it depends on large window size, and therefore reduces the spatial resolution of FR estimation. In order to realize optimal noise suppression with minimized resolution loss, the total variation (TV) denoising method is applied in this paper. By testing the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) full-pol datasets, TV and AF are compared and validated. Results using synthetic and real data show that, after TV denoising, the FR can be recovered with high spatial resolution and the noise level in estimated FR is reduced more effectively than that after AF.

Multicomponent microseismic data denoising by 3D shearlet transform

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A45-A51 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Noise Suppression ◽  
Random Noise ◽  
Real Data ◽  
Fracture Initiation ◽  
Multidimensional Data ◽  
Data Sets ◽  
Denoising Method ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
Significant Step

The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data because it captures details of signals at different scales and orientations, which benefits signal and noise separation. We have developed a novel processing strategy based on a signal-detection operator that can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of 3C microseismic signals into account. We perform tests on synthetic and real data sets and determine that the proposed method can effectively remove random noise and preserve weak signals.

scholarly journals Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Min Cao ◽  
Dongping Ming ◽  
Lu Xu ◽  
Ju Fang ◽  
Lin Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Analysis ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Texture Feature ◽  
Window Size ◽  
Remote Sensing Image ◽  
Selection Method ◽  
Image Texture ◽  
Scale Selection

Image texture is an important visual cue in image processing and analysis. Texture feature expression is an important task of geo-objects expression by using a high spatial resolution remote sensing image. Texture features based on gray level co-occurrence matrix (GLCM) are widely used in image spatial analysis where the spatial scale is especially of great significance. Based on the Fourier frequency-spectral analysis, this paper proposes an optimal scale selection method for GLCM. Different subset textures are firstly upscaled by GLCM with different window sizes. Then the multiscale texture feature images are converted into the frequency domain by Fourier transform. Consequently, the radial distribution and angular distribution curves changing with different window sizes from spectrum energy can be achieved, by which the texture window size can be selected. In order to verify the validity of this proposed texture scale selection method, this paper uses high-resolution fusion images to classify land cover based on multiscale texture expression. The results show that the proposed method combining frequency-spectral analysis-based texture scale selection can guarantee the quality and accuracy of the classification, which further proves the effectiveness of optimal texture window size selection method bases on frequency spectrum analysis. Other than scale selection in spatial domain, this paper casts a novel idea for texture scale selection in the frequency domain, which is meant for scale processing of remote sensing image.

scholarly journals Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah

2021 ◽  
Author(s):  
Rigobert Tibi ◽  
Patrick Hammond ◽  
Ronald Brogan ◽  
Christopher J. Young ◽  
Keith Koper
Keyword(s):  
Input Signal ◽  
Noise Suppression ◽  
Fundamental Problem ◽  
Real Data ◽  
Denoising Method ◽  
Similar Frequency ◽  
Waveform Data ◽  
Seismic Waveform ◽  
Band Pass ◽  
Music Information

ABSTRACT Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ∼5  dB, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ∼0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ∼2–5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ∼15  dB.

scholarly journals Window length selection for optimum slowness resolution of the local-slant-stack transform

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. V31-V40 ◽  
Author(s):  
Sergi Ventosa ◽  
Carine Simon ◽  
Martin Schimmel
Keyword(s):  
Spatial Resolution ◽  
Spectral Characteristics ◽  
Window Size ◽  
Real Data ◽  
Window Length ◽  
Data Set ◽  
Automatic Data ◽  
Filter Performance ◽  
Spatial Window ◽  
Selection Of

One of the most critical decisions in the design of a local-slant-stack transform (LSST) is the selection of its aperture, or more precisely, the selection of the appropriate number of traces and their weighting coefficients for each slant stack. The challenge is to achieve a good compromise between the slowness and the spatial resolution. Conventionally, the window size is chosen in a more intuitive manner by visual inspection and some limited tests. We analyzed the LSST to establish rigorous criteria for the window selection to achieve the optimum slowness and spatial resolution in the transformed domain for a given data set. For this purpose, we estimated the slowness resolution in the LSST domain as a function of the spatial-window bandwidth and of the spectral characteristics of the waves. For a wave with a given bandpass spectrum, the slowness resolution, the stopband attenuation, and the wavefront-tracking capability are determined by the spatial window. For narrowband signals, the spatial window must be larger than the stopband bandwidth divided by the desired slowness resolution and the central frequency of the band. For wideband signals, the window length is determined by the lowest frequency components. Much longer windows can only be used when the slowness and the amplitude variations of the wavefront trajectories are small. We validated our approach with a synthetic example and applied it to a wide-angle seismic profile to show the filter performance on real data in which the LSST-window length is determined in an automatic, data-adaptive manner.

scholarly journals The Influence of CLBP Window Size on Urban Vegetation Type Classification Using High Spatial Resolution Satellite Images

2020 ◽  
Vol 12 (20) ◽  
pp. 3393
Author(s):  
Zhou Chen ◽  
Xianyun Fei ◽  
Xiangwei Gao ◽  
Xiaoxue Wang ◽  
Huimin Zhao ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Vegetation Type ◽  
Window Size ◽  
Urban Heat Islands ◽  
Spectral Information ◽  
Urban Vegetation ◽  
Vegetation Types ◽  
Human Beings ◽  
Type Classification

Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings’ mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary pattern (CLBP) texture features classifying urban vegetation based on high spatial-resolution WorldView-2 images in areas of Shanghai (China) and Lianyungang (Jiangsu province, China). To demonstrate the stability and universality of different CLBP window textures, two study areas were selected. Using spectral information alone and spectral information combined with texture information, imagery is classified using random forest (RF) method based on vegetation type, showing that use of spectral information with CLBP window textures can achieve 7.28% greater accuracy than use of only spectral information for urban vegetation type classification, with accuracy greater for single vegetation types than for mixed ones. Optimal window sizes of CLBP textures for grass, shrub, arbor, shrub-grass, arbor-grass, and arbor-shrub-grass are 3 × 3, 3 × 3, 11 × 11, 9 × 9, 9 × 9, 7 × 7 for urban vegetation type classification. Furthermore, optimal CLBP window size is determined by the roughness of vegetation texture.

scholarly journals MULTISPECTRAL AND PANCHROMATIC REGISTRATION OF ALSAT-2 IMAGES USING DENSE VECTOR MATCHING FOR PAN-SHARPENING PROCESS

2018 ◽  
Vol XLII-2 ◽  
pp. 149-153 ◽  
Author(s):  
I. Boukerch ◽  
N. Farhi ◽  
M. S. Karoui ◽  
K. Djerriri ◽  
R. Mahmoudi
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Real Data ◽  
Remote Sensing Image ◽  
High Spectral Resolution ◽  
Multispectral Imagery ◽  
Registration Error ◽  
Panchromatic Image ◽  
Remote Sensing Image Processing

The pan-sharpening is a widely used operation in remote sensing image processing, this operation aims at combining an observable high spatial resolution panchromatic image with a multispectral one, to generate an unobservable image with the high spatial resolution of the former and a high spectral resolution of the latter. Generally, papers dealing with this problem omit the geometric part and suppose that these images are perfectly aligned, which is not necessarily the case for the raw imagery, where even the different bands in the multispectral imagery are misaligned. In this paper, new method for multispectral and panchromatic image registration is proposed to deal with the misalignment problem that reduces the pansharpening quality. This method called Dense Vector Matching (DVM) is based on the matching of a whole line-vector or column-vector from a reference band with the corresponding vector in a target band. DVM is applied on real data and has given acceptable results, where the QNR index of the pan-sharpening is better for images after band registration, also the registration error is reduced to sub-pixel using the proposed approach.

scholarly journals Sentinel-2 e campionamenti in situ per il monitoraggio delle acque marine dell’Abruzzo: primi risultati

2020 ◽  
pp. 557-568
Author(s):  
Carla Ippoliti ◽  
Susanna Tora ◽  
Carla Giansante ◽  
Romolo Salini ◽  
Federico Filipponi ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Real Data ◽  
High Time ◽  
Sedimentary Processes ◽  
Time Frequency ◽  
Sentinel 2

In this study, the estimate of chlorophyll "a" and the dispersion of sediment in the sea, calculated from Sentinel-2, was compared with real data acquired in situ by a multiparametric probe, along the Abruzzo coast. The ultimate goal is to optimize parameters and algorithms to be able to derive concentration maps of chlorophyll and suspended solids from satellite, taking advantage of the high time frequency and high spatial resolution of the detections. This information is of particular relevance for aquaculture activities, for monitoring water quality and for analyzing sedimentary processes.

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