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

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.

Download Full-text

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

Journal of Innovative Image Processing - December 2019 ◽

10.36548/jiip.2021.4.001 ◽

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.

Download Full-text

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

10.5194/egusphere-egu21-10580 ◽

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

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.&#160; 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.&#160;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.&#160;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.&#160;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.&#160; 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.

Download Full-text

Tropical Cyclone Wind Speed Retrieval from Dual-Polarization Sentinel-1 EW Mode Products

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-19-0148.1 ◽

2020 ◽

Vol 37 (9) ◽

pp. 1713-1724

Author(s):

Yuan Gao ◽

Changlong Guan ◽

Jian Sun ◽

Lian Xie

Keyword(s):

Wind Speed ◽

Tropical Cyclone ◽

Noise Removal ◽

Cross Polarization ◽

High Wind ◽

Dual Polarization ◽

High Wind Speed ◽

Wind Speeds ◽

Hurricane Wind ◽

The Cross

AbstractRecent studies indicate that the cross-polarization synthetic aperture radar (SAR) images have the ability of retrieving high wind speed on ocean surface without wind direction input. This study presents a new approach for tropical cyclone (TC) wind speed retrieval utilizing thermal-noise-removed Sentinel-1 dual-polarization (VV + VH) Extra-Wide Swath (EW) Mode products. Based on 20 images of 9 TCs observed in the 2016 and 2018 and SAR-collocated European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) data and the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s (HRD) Real-time Hurricane Wind Analysis System (H*Wind) data, a subswath-based geophysical model function (GMF) Sentinel-1 EW Mode Wind Speed Retrieval Model after Noise Removal (S1EW.NR) is developed and validated statistically. TC wind speed is retrieved by using the proposed GMF and the C-band model 5.N (CMOD5.N). The results show that the wind speeds retrieved by the S1EW.NR model are in good agreement with wind references up to 31 m s−1. The correlation coefficient, bias, and standard deviation between the retrieval results and reference wind speeds are 0.74, −0.11, and 3.54 m s−1, respectively. Comparison of the wind speeds retrieved from both channels suggests that the cross-polarized signal is more suitable for high–wind speed retrieval, indicating the promising capability of cross-polarization SAR for TC monitoring.

Download Full-text

Sentinel-1 additive noise removal from cross-polarization extra-wide TOPSAR with dynamic least-squares

Remote Sensing of Environment ◽

10.1016/j.rse.2020.111982 ◽

2020 ◽

Vol 248 ◽

pp. 111982

Author(s):

Peter Q. Lee ◽

Linlin Xu ◽

David A. Clausi

Keyword(s):

Least Squares ◽

Additive Noise ◽

Noise Removal ◽

Cross Polarization

Download Full-text

Thermal Noise Removal From Polarimetric Sentinel-1 Data

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3050921 ◽

2021 ◽

pp. 1-1

Author(s):

Lucio Mascolo ◽

Juan M. Lopez-Sanchez ◽

Shane R. Cloude

Keyword(s):

Thermal Noise ◽

Noise Removal

Download Full-text

Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images over Sea Ice

10.20944/preprints202006.0092.v1 ◽

2020 ◽

Author(s):

Yan Sun ◽

Xiaoming Li

Keyword(s):

Sea Ice ◽

Thermal Noise ◽

Multiplicative Noise ◽

The Arctic ◽

Polar Regions ◽

Cross Polarization ◽

Denoising Method ◽

Residual Noise ◽

Ice Conditions ◽

Ice Detection

Sentinel-1 (S1) extra-wide (EW) swath data in cross-polarization (horizontal-vertical, HV or vertical-horizontal, VH) are strongly affected by the scalloping effect and thermal noise, particularly over areas with weak backscattered signals, such as sea surfaces. Although noise vectors in both the azimuth and range directions are provided in the standard S1 EW data for subtraction, the residual thermal noise still significantly affects sea ice detection by the EW data. In this paper, we improve the denoising method developed in previous studies to remove the additive noise for the S1 EW data in cross-polarization. Furthermore, we propose a new method for eliminating the residual noise (i.e. multiplicative noise) at the sub-swath boundaries of the EW data, which cannot be well processed by simply subtracting the reconstructed 2-D noise field. The proposed method of removing both the additive and multiplicative noise was applied to EW HV-polarized images processed using different Instrument Processing Facility (IPF) versions. The results suggest that the proposed algorithm significantly improves the quality of EW HV-polarized images under various sea ice conditions and sea states in marginal ice zone (MIZ) of the Arctic. This is of great support for the utilization of cross-polarization SAR images in wide swaths for intensive sea ice monitoring in polar regions.

Download Full-text