Thermal noise removal in hybrid polarimetry SAR data

2016 ◽  
Author(s):  
J. V. D. Suneela Mishra ◽  
Tapan Misra
Keyword(s):  
Thermal Noise ◽  
Noise Removal ◽  
Sar Data

Convolutional Neural Networks for Classification of Sea Ice Types in Sentinel-1 SAR Data

2021 ◽  
Author(s):  
Anton Korosov ◽  
Hugo Boulze ◽  
Julien Brajard
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Classification Accuracy ◽  
Thermal Noise ◽  
Noise Removal ◽  
The Arctic ◽  
First Year ◽  
Sar Data ◽  
The Impact

<p>A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented.  The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on a dataset from winter 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 91.6%. The error is a bit higher for young ice (76%) and first-year ice (84%). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data.</p><p> </p><p>Our study demonstrates that CNN can be successfully applied for classification of sea ice types in SAR data. The algorithm is applied in small sub-images extracted from a SAR image after preprocessing including thermal noise removal. Validation shows that the errors are mostly attributed to coarse resolution of ice charts or misclassification of training data by human experts.</p><p> </p><p>Several sensitivity experiments were conducted for testing the impact of CNN architecture, hyperparameters, training parameters and data preprocessing on accuracy. It was shown that a CNN with three convolutional layers, two max-pool layers and three hidden dense layers can be applied to a sub-image with size 50 x 50 pixels for achieving the best results. It was also shown that a CNN can be applied to SAR data without thermal noise removal on the preprocessing step. Understandably, the classification accuracy decreases to 89% but remains reasonable.</p><p> </p><p>The main advantages of the new algorithm are the ability to classify several ice types, higher classification accuracy for each ice type and higher speed of processing than in the previous studies. The relative simplicity of the algorithm (both texture analysis and classification are performed by CNN) is also a benefit. In addition to providing ice type labels, the algorithm also derives the probability of belonging to a class. Uncertainty of the method can be derived from these probabilities and used in the assimilation of ice type in numerical models. </p><p><br>Given the high accuracy and processing speed, the CNN-based algorithm is included in the Copernicus Marine Environment Monitoring Service (CMEMS) for operational sea ice type retrieval for generating ice charts in the Arctic Ocean. It is already released as an open source software and available on Github: https://github.com/nansencenter/s1_icetype_cnn.</p>

scholarly journals Noise Removal Based on Tensor Modelling for Hyperspectral Image Classification

2018 ◽  
Vol 10 (9) ◽  
pp. 1330 ◽  
Author(s):  
Salah Bourennane ◽  
Caroline Fossati ◽  
Tao Lin
Keyword(s):  
White Noise ◽  
Spatial Resolution ◽  
Thermal Noise ◽  
Hyperspectral Image ◽  
Additive White Gaussian Noise ◽  
Wiener Filter ◽  
Noise Removal ◽  
Parameters Estimation ◽  
Photon Noise ◽  
Noise Parameters

With the current state-of-the-art computer aided manufacturing tools, the spatial resolution of hyperspectral sensors is becoming increasingly higher thus making it easy to obtain much more detailed information of the scene captured. However, the improvement of the spatial resolution also brings new challenging problems to address with signal dependent photon noise being one of them. Unlike the signal independent thermal noise, the variance of photon noise is dependent on the signal, therefore many denoising methods developed for the stationary noise cannot be applied directly to the photon noise. To make things worse, both photon and thermal noise coexist in the captured hyperspectral image (HSI), thus making it more difficult to whiten noise. In this paper, we propose a new denoising framework to cope with signal dependent nonwhite noise (SDNW), Pre-estimate—Whitening—Post-estimate (PWP) loop, to reduce both photon and thermal noise in HSI. Previously, we proposed a method based on multidimensional wavelet packet transform and multi-way Wiener filter which performs both white noise and spectral dimensionality reduction, referred to as MWPT-MWF, which was restricted to white noise. We get inspired from this MWPT-MWF to develop a new iterative method for reducing photon and thermal noise. Firstly, the hyperspectral noise parameters estimation (HYNPE) algorithm is used to estimate the noise parameters, the SD noise is converted to an additive white Gaussian noise by pre-whitening procedure and then the whitened HSI is denoised by the proposed method SDNW-MWPT-MWF. As comparative experiments, the Multiple Linear Regression (MLR) based denoising method and tensor-based Multiway Wiener Filter (MWF) are also used in the denoising framework. An HSI captured by Reflective Optics System Imaging Spectrometer (ROSIS) is used in the experiments and the denoising performances are assessed from various aspects: the noise whitening performance, the Signal-to-Noise Ratio (SNR), and the classification performance. The results on the real-world airborne hyperspectral image HYDICE (Hyperspectral Digital Imagery Collection Experiment) are also presented and analyzed. These experiments show that it is worth taking into account noise signal-dependency hypothesis for processing HYDICE and ROSIS HSIs.

Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel

2018 ◽  
Vol 56 (3) ◽  
pp. 1555-1565 ◽  
Author(s):  
Jeong-Won Park ◽  
Anton A. Korosov ◽  
Mohamed Babiker ◽  
Stein Sandven ◽  
Joong-Sun Won
Keyword(s):  
Thermal Noise ◽  
Noise Removal ◽  
Cross Polarization ◽  
Polarization Channel

scholarly journals Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm

2021 ◽  
Vol 3 (4) ◽  
pp. 284-297
Author(s):  
B. Vivekanandam
Keyword(s):  
Thermal Noise ◽  
Noise Removal ◽  
Common Type ◽  
Imaging Systems ◽  
Research Report ◽  
Median Filtering ◽  
Challenging Problem ◽  
Sensor Devices ◽  
Original Picture ◽  
Filtering Approach

Thermal noise is the most common type of contamination in digital image acquisition operations, and is caused by the temperature condition of the industrial sensor devices used in the process. When it comes to picture improvement, removing noise from the image is one of the most crucial steps. However, in image processing, it is more critical to retain the characteristics of the original picture while eliminating the noise. Thermal noise removal is a challenging problem in image denoising. This article provides a strategy based on a Hybrid Adaptive Median (HAM) filtering approach for removing thermal noise from the image output of an industrial sensor. The demonstration of this proposed approach's ability, is to successfully detect and reduce thermal noise. In addition, this study examines an adaptive hybrid adaptive median filtering approach that has significant computational advantages, making it highly practical. Finally, this research report on experiments shows the high-quality industrial sensor imaging systems that have been successfully implemented in the real world.

scholarly journals Thermal Noise Removal From Polarimetric Sentinel-1 Data

2021 ◽  
pp. 1-1
Author(s):  
Lucio Mascolo ◽  
Juan M. Lopez-Sanchez ◽  
Shane R. Cloude
Keyword(s):  
Thermal Noise ◽  
Noise Removal

Ultra-spatially resolved synchrotron powered FT-IR microspectroscopy of individual cells of biological material

1995 ◽  
Vol 53 ◽  
pp. 884-885
Author(s):  
David L. Wetzel ◽  
John A. Reffner ◽  
Gwyn P. Williams
Keyword(s):  
Thermal Noise ◽  
Spot Size ◽  
Acquisition Time ◽  
Thermal Source ◽  
Signal To Noise ◽  
Spatially Resolved ◽  
Good Spatial Resolution ◽  
Small Spot ◽  
Ft Ir ◽  
Ir Microspectroscopy

Synchrotron radiation is 100 to 1000 times brighter than a thermal source such as a globar. It is not accompanied with thermal noise and it is highly directional and nondivergent. For these reasons, it is well suited for ultra-spatially resolved FT-IR microspectroscopy. In efforts to attain good spatial resolution in FT-IR microspectroscopy with a thermal source, a considerable fraction of the infrared beam focused onto the specimen is lost when projected remote apertures are used to achieve a small spot size. This is the case because of divergence in the beam from that source. Also the brightness is limited and it is necessary to compromise on the signal-to-noise or to expect a long acquisition time from coadding many scans. A synchrotron powered FT-IR Microspectrometer does not suffer from this effect. Since most of the unaperatured beam’s energy makes it through even a 12 × 12 μm aperture, that is a starting place for aperture dimension reduction.

EFFECT OF CELLULAR MEMBRANE RESISTIVITY INHOMOGENEITY ON THE THERMAL NOISE-LIMIT

2015 ◽  
Vol 11 (3) ◽  
pp. 3171-3183
Author(s):  
Gyula Vincze
Keyword(s):  
Field Strength ◽  
Thermal Noise ◽  
Cellular Membrane ◽  
Thermal Limit ◽  
Low Frequencies ◽  
Membrane Resistivity ◽  
Noise Limit ◽  
The Asymmetry ◽  
Characteristic Field

Our objective is to generalize the Weaver-Astumian (WA) and Kaune (KA) models of thermal noise limit to the case ofcellular membrane resistivity asymmetry. The asymmetry of resistivity causes different effects in the two models. In the KAmodel, asymmetry decreases the characteristic field strength of the thermal limit over and increases it below the breakingfrequency (10  m), while asymmetry decreases the spectral field strength of the thermal noise limit at all frequencies.We show that asymmetry does not change the character of the models, showing the absence of thermal noise limit at highand low frequencies in WA and KA models, respectively.

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