Sea ice classification using wide-swath SAR data considering incidence angle depenency of backscatter intensity and surface texture

2020 ◽  
Author(s):  
Wenkai Guo ◽  
Polona Itkin ◽  
Johannes Philipp Lohse
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Surface Texture ◽  
Classification Scheme ◽  
Incidence Angle ◽  
Texture Features ◽  
Angle Variation ◽  
Backscatter Intensity ◽  
Sar Data ◽  
Wide Swath

<p>In this study we develop a novel sea ice classification scheme based on remote sensing Synthetic-aperture Radar (SAR) data, and use it to classify sea ice types over the spatial and temporal range of the Norwegian Young sea ICE cruise (N-ICE2015). Ice type classification will be conducted on wide-swath SAR datasets including RADARSAT-2 and Sentinel-1 data. We use a classification scheme that takes into account different rates of decrease in backscatter intensity with incidence angle variation for different classes. In addition, it examines texture features of different sea ice types, and also variations of surface texture with changing incidence angles, and incorporates this relationship into the classification process. Sea ice classifications using high-resolution SAR images collected over the same period and also field data retrieved from the N-ICE2015 expedition will be used for ground truthing. Earlier N-ICE2015 studies with high resolution SAR and deformation suggest high lead and pressure ridge formation. We will use our lower-resolution results to explore potential increase in the fraction of deformed and lead ice from January to June 2015 in the region north of Svalbard.</p>

scholarly journals Is Radar Phase Information Useful for Sea Ice Detection in the Marginal Ice Zone?

2020 ◽  
Vol 12 (11) ◽  
pp. 1847
Author(s):  
Fuhong Ding ◽  
Hui Shen ◽  
William Perrie ◽  
Yijun He
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Phase Difference ◽  
Incidence Angle ◽  
The Arctic ◽  
Sar Images ◽  
Ecological Processes ◽  
Additional Tool ◽  
Marginal Ice Zone ◽  
Ice Detection

With continuing sea ice reductions in the Arctic, dynamic physical and ecological processes have more active roles compared to the ice-locked, isolated Arctic Ocean of previous decades. To better understand these changes, observations of high-resolution sea ice conditions are needed. Remote sensing is a useful tool for observations in the harsh Arctic environment. For unsupervised ice detection, we demonstrate the promising value of radar phase difference from polarimetric radar measurements in this study, based on full polarimetric complex RADARSAT-2 SAR images in the marginal ice zone. It is demonstrated that the phase difference from co-polarized and cross-polarized synthetic aperture radar (SAR) images show promising capability for high resolution sea ice discrimination from open water. In particular, the phase difference shows superior potential for the detection of frazil ice compared to the traditional methodology based on the radar intensity ratio. The relationship between phase difference and radar incidence angle is also analyzed, as well as the potential influence of high sea state. The new methodology provides an additional tool for ice detection. In order to make the best use of this tool, directions for further studies are discussed for operational ice detection and possible ice classification.

scholarly journals Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle

2020 ◽  
pp. 1-11
Author(s):  
Johannes Lohse ◽  
Anthony P. Doulgeris ◽  
Wolfgang Dierking
Keyword(s):  
Sea Ice ◽  
Incidence Angle ◽  
Mean Value ◽  
Surface Type ◽  
Validation Data ◽  
Dependent Effect ◽  
Gaussian Probability Density Function ◽  
Backscatter Intensity ◽  
The Mean ◽  
Sar Imagery

Abstract Automated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is complicated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instruments. A global correction does not consider that different surface types show different rates of decrease in backscatter intensity. Here, we introduce a supervised classification algorithm that directly incorporates the surface-type dependent effect of IA. We replace the constant mean vector of a Gaussian probability density function in a Bayesian classifier with a linearly variable mean. During training, the classifier first retrieves the slope and intercept of the linear function describing the mean value and then calculates the covariance matrix as the mean squared deviation relative to this function. The IA dependence is no longer treated as an image property but as a class property. Based on training and validation data selected from overlapping SAR and optical images, we evaluate the proposed method in several case studies and compare to other classification algorithms for which a global IA correction is applied during pre-processing. Our results show that the inclusion of the per-class IA sensitivity can significantly improve the performance of the classifier.

scholarly journals Feasibility Study on Estimation of Sea Ice Drift from KOMPSAT-5 and COSMO-SkyMed SAR Images

2021 ◽  
Vol 13 (20) ◽  
pp. 4038
Author(s):  
Jeong-Won Park ◽  
Hyun-Cheol Kim ◽  
Anton Korosov ◽  
Denis Demchev ◽  
Stefano Zecchetto ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Large Scale ◽  
Daily Basis ◽  
Drift Field ◽  
X Band ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Polar Ocean ◽  
Wide Swath

Estimating the sea ice drift field is of importance in both scientific study and activities in the polar ocean. Ice motion is being tracked at large scale (10 km and larger) on a daily basis; however, a higher resolution product is desirable for more reliable monitoring of rapid changes in sea ice. The use of wide-swath SAR has been extensively studied; yet, recent high-resolution X-band SAR sensors have not been tested enough. We examine the feasibility of KOMPSAT-5 and COSMO-SkyMed for retrieving sea ice motion by using the dataset of the MOSAiC expedition. The ice drift match-ups extracted from consecutive SAR image pairs and buoys for more than seven months in the central Arctic were used for a performance evaluation and validation. In addition to individual tests for KOMPSAT-5 and COSMO-SkyMed, a cross-sensor combination of two sensors was tested to overcome the drawback, a relatively long revisit time of high-resolution SAR. The experimental results show that higher accuracies are achievable from both single- and cross-sensor configurations of high-resolution X-band SARs compared to wide-swath C-band SARs, and that sub-daily monitoring is feasible from the cross-sensor approach.

scholarly journals Cross-platform application of a sea ice classification method considering incident angle dependency of backscatter intensity and its use in separating level and deformed ice

2021 ◽  
Author(s):  
Wenkai Guo ◽  
Polona Itkin ◽  
Johannes Lohse ◽  
Malin Johansson ◽  
Anthony Paul Doulgeris
Keyword(s):  
Sea Ice ◽  
Expert Knowledge ◽  
Incident Angle ◽  
Classification Method ◽  
Backscatter Intensity ◽  
Cross Platform ◽  
Sar Data ◽  
The Cross ◽  
Training Sets

Abstract. Wide-swath C-band synthetic aperture radar (SAR) has been used for sea ice classification and estimates of sea ice drift and deformation since it first became widely available in the 1990s. Here, we examine the potential to distinguish surface features created by sea ice deformation using ice type classification of SAR data. To perform this task with extended spatial and temporal coverage, we investigate the cross-platform transferability between training sets derived from Sentinel-1 Extra Wide (S1 EW) and RADARSAT-2 (RS2) ScanSAR Wide A (SCWA) and Fine Quad-polarimetric (FQ) data, as the same radiometrically calibrated backscatter coefficients are expected from these two C-band SAR platforms. For this, we use a novel sea ice classification method developed based on Arctic-wide S1 EW training, which considers the ice-type-dependent change of SAR backscatter intensity with incident angle (IA). This study focuses on the region near Fram Strait north of Svalbard to utilize expert knowledge of ice conditions from co-authors who participated in the Norwegian young sea ICE (N-ICE2015) expedition in the region. Separate training sets for S1 EW, RS2 SCWA and RS2 FQ data are derived using manually drawn polygons of different ice types, and are used to re-train the classifier. Results show that although the best classification accuracy is achieved for each dataset using its own training, different training sets yield similar results and IA slopes, with the exception of leads with calm open water, nilas or newly formed ice (the “leads”' class). This is found to be caused by different noise floor configurations of S1 and RS2 data, which lead to different IA slopes of this class. This indicates that dataset-specific re-training is needed for leads in the cross-platform application of the classifier. Based on the classifier thus re-trained for each dataset, the classification scheme is altered to target the separation of level and deformed ice, which enables direct comparison with independently derived sea ice deformation maps. The comparisons show that the classification of C-band SAR can be used to distinguish areas of ice divergence occupied by leads, young ice and level first-year ice (LFYI). However, it has limited capacity in delineating areas of ice deformation due to ambiguities in ice types represented by classes with higher backscatter intensities. This study provides reference to future studies seeking cross-platform application of training sets so they are fully utilized, and we expect further development of the classifier and the inclusion of other SAR datasets to enable image classification-based ice deformation detection using only satellite SAR data.

scholarly journals Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks

2020 ◽  
Vol 12 (13) ◽  
pp. 2165 ◽  
Author(s):  
Hugo Boulze ◽  
Anton Korosov ◽  
Julien Brajard
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Learning Algorithm ◽  
Computing Time ◽  
Texture Features ◽  
Random Forest Classifier ◽  
Synthetic Aperture ◽  
First Year ◽  
Sar Data

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 two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90.5% for the 2018-dataset and 91.6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%/76% accuracy in 2018/2020) and first-year ice (86%/84% accuracy in 2018/2020). 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. The code is publicly available.

scholarly journals AN ADAPTIVE SHIP DETECTION ALGORITHM FOR HRWS SAR IMAGES UNDER COMPLEX BACKGROUND: APPLICATION TO SENTINEL1A DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 497-503
Author(s):  
G. He ◽  
Z. Xia ◽  
H. Chen ◽  
K. Li ◽  
Z. Zhao ◽  
...  
Keyword(s):  
High Resolution ◽  
False Alarm ◽  
False Alarm Rate ◽  
Texture Features ◽  
Detection Algorithm ◽  
Support Vector ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Ship Detection ◽  
Wide Swath

Real-time ship detection using synthetic aperture radar (SAR) plays a vital role in disaster emergency and marine security. Especially the high resolution and wide swath (HRWS) SAR images, provides the advantages of high resolution and wide swath synchronously, significantly promotes the wide area ocean surveillance performance. In this study, a novel method is developed for ship target detection by using the HRWS SAR images. Firstly, an adaptive sliding window is developed to propose the suspected ship target areas, based upon the analysis of SAR backscattering intensity images. Then, backscattering intensity and texture features extracted from the training samples of manually selected ship and non-ship slice images, are used to train a support vector machine (SVM) to classify the proposed ship slice images. The approach is verified by using the Sentinl1A data working in interferometric wide swath mode. The results demonstrate the improvement performance of the proposed method over the constant false alarm rate (CFAR) method, where the classification accuracy improved from 88.5 % to 96.4 % and the false alarm rate mitigated from 11.5 % to 3.6 % compared with CFAR respectively.

scholarly journals Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 552
Author(s):  
Johannes Lohse ◽  
Anthony P. Doulgeris ◽  
Wolfgang Dierking
Keyword(s):  
Sea Ice ◽  
Incident Angle ◽  
Texture Features ◽  
Data Set ◽  
Backscatter Intensity ◽  
Medium Resolution ◽  
Suitable Combination ◽  
Reliable Classification ◽  
Occurrence Matrix

Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice.

Principle and practice of high-resolution imaging with a field-emission TEM

1992 ◽  
Vol 50 (1) ◽  
pp. 138-139
Author(s):  
Max T. Otten ◽  
Wim M.J. Coene
Keyword(s):  
High Resolution ◽  
Field Emission ◽  
Incidence Angle ◽  
Temporal Coherence ◽  
Energy Spread ◽  
High Resolution Imaging ◽  
Major Improvement ◽  
High Resolution Images ◽  
Resolution Imaging

High-resolution imaging with a LaB6 instrument is limited by the spatial and temporal coherence, with little contrast remaining beyond the point resolution. A Field Emission Gun (FEG) reduces the incidence angle by a factor 5 to 10 and the energy spread by 2 to 3. Since the incidence angle is the dominant limitation for LaB6 the FEG provides a major improvement in contrast transfer, reducing the information limit to roughly one half of the point resolution. The strong improvement, predicted from high-resolution theory, can be seen readily in diffractograms (Fig. 1) and high-resolution images (Fig. 2). Even if the information in the image is limited deliberately to the point resolution by using an objective aperture, the improved contrast transfer close to the point resolution (Fig. 1) is already worthwhile.

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