Download or read book Toward Automated Ice water Classification on Large Northern Lakes Using RADARSAT 2 Synthetic Aperture Radar Imagery written by Marie Hoekstra and published by . This book was released on 2018 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability. These changes are expected to have implications for both human and environmental systems. Additionally, monitoring lake ice cover is required to enable more reliable weather forecasting across lake-rich northern latitudes. Currently the Canadian Ice Service (CIS) monitors lakes using RADARSAT-2 SAR (synthetic aperture radar) and optical imagery through visual interpretation, with total lake ice cover reported weekly as a fraction out of ten. An automated method of classification would allow for more detailed records to be delivered operationally. In this research, the Iterative Region Growing using Semantics (IRGS) approach has been employed to perform ice-water classification on 61 RADARSAT-2 scenes of Great Bear Lake and Great Slave Lake over a three year period. This approach first locally segments homogeneous regions in an image, then merges similar regions into classes across the entire scene. These classes are manually labelled by the user, however automated labelling capability is currently in development. An accuracy assessment has been performed on the classification results, comparing outcomes with user-generated reference data as well as the CIS fraction reported at the time of image acquisition. The overall average accuracy of the IRGS method for this dataset is 92%, demonstrating the potential of this semi-automated method to provide detailed and reliable lake ice cover information.

Download or read book Towards Automated Lake Ice Classification Using Dual Polarization RADARSAT SAR Imagery written by Junqian Wang and published by . This book was released on 2018 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lake ice, as one of the most important component of the cryosphere, is a valuable indicator of climate change and variability. The Laurentian Great Lakes are the world's largest supply of freshwater and their ice cover has a major impact on regional weather and climate, ship navigation, and public safety. Monitoring detailed ice conditions on large lakes requires the use of satellite-borne synthetic aperture radar (SAR) data that provide all-weather sensing capabilities, high resolution, and large spatial coverage. Ice experts at the Canadian Ice Service (CIS) have been manually producing operational Great Lakes image analysis charts based on visual interpretation of the SAR images. Ice services such as the CIS would greatly benefit from the availability of an automated or semi-automated SAR ice classification algorithm. We investigated the performance of the unsupervised segmentation algorithm “glocal” iterative region growing with semantics (IRGS) for lake ice classification using dual polarized RADARSAT-2 imagery. Here, the segmented classes with arbitrary labels are manually labelled based on visual interpretation. IRGS was tested on 26 RADARSAT-2 scenes acquired over Lake Erie during winter 2014, and the results were validated against the manually produced CIS image analysis charts. Analysis of various case studies indicated that the “glocal” IRGS algorithm can provide a reliable ice-water classification using dual polarized images with a high overall accuracy of 90.2%. The improvement of using dual-pol as opposed to single-pol images for ice-water discrimination was also demonstrated. For lake ice type classification, most thin ice types were effectively identified but thick and very thick lake ice were often confused due to the ambiguous relation between backscatter and ice types. Texture features could be included for further improvement. Overall, our “glocal” IRGS classification results are close to visual interpretation by ice analysts and would have expected to be closer if they could draw ice charts at a more detailed level.

Download or read book Automated Ice water Classification Using Dual Polarization Synthetic Aerture Radar Imagery written by Steven Leigh and published by . This book was released on 2013 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use.

Download or read book RADARSAT 2 Polarimetric Radar Imaging for Lake Ice Mapping written by Feng Pan and published by . This book was released on 2017 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Changes in lake ice dates and duration are useful indicators for assessing long-term climate trends and variability in northern countries. Lake ice cover observations are also a valuable data source for predictions with numerical ice and weather forecasting models. In recent years, satellite remote sensing has assumed a greater role in providing observations of lake ice cover extent for both modeling and climate monitoring purposes. Polarimetric radar imaging has become a promising tool for lake ice mapping at high latitudes where cloud cover and polar darkness severely limit observations from optical sensors. In this study, we assessed and characterized the physical scattering mechanisms of lake ice from fully polarimetric RADARSAT-2 datasets obtained over Great Bear Lake, Canada, with the intent of classifying open water and ice cover during the freeze-up and break-up periods. Model-based and eigen-based decompositions were employed to construct the coherency matrix into deterministic scattering mechanisms, and secondary physical parameters were generated following the polarimetric decompositions. This study presents an application of the Markov Random Field by introducing radar signals and polarimetric parameters as features. These features were labeled using the entropy-alpha Wishart classifier. We show that the selected polarimetric parameters can help with interpretation of radar-ice/water interactions and can be used successfully for water-ice segmentation. As more satellite SAR sensors are being launched or planned, such as the Sentinel-1a/b series and the upcoming RADARSAT Constellation Mission, the rapid volume growth of data and their analysis require the development of robust automated algorithms. The approach developed in this study was therefore designed with the intent of moving towards fully automated mapping of lake ice for consideration by ice services.

Download or read book Sea Ice Classification Using Synthetic Aperture Radar written by and published by . This book was released on 1990 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study employs Synthetic Aperture Radar (SAR) imagery from the Marginal Ice Zone Experiment (MIZEX) 1987 to identify an optimal set of statistical descriptors that accurately classify three types of ice (first-year, multiyear, odden) and open water. Two groups of statistics, univariate and texture, are compared and contrasted with respect to their skill in classifying the ice types and open water. Individual statistical descriptors are subjected to principal component analysis and discriminant analysis. Principal component analysis was of little use in understanding features of each ice and open water group. Discriminant analysis was valuable in identifying which statistics held the most power. When combined, univariate and texture statistics classified the groups with 89.5% accuracy, univariate alone with 86.8% accuracy and texture alone with 75.4% accuracy. Range and inertia were the strongest univariate and texture discriminators with 74.6% and 50.8% accuracy, respectively. Despite the use of a non-calibrated SAR, univariate statistics were able to classify the images with greater accuracy than texture statistics.

Download or read book Sea Ice SAR Imagery Classification and Regression Based On Convolutional Neural Networks written by Yan Xu and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the global warming, there have been signficant reductions in the ice extent and ice thickness in the Arctic and marginal seas. Monitoring these changes in sea ice is very important for human activities including weather forecasting, natural-resource extraction, and ship navigation. Of the various sea ice monitoring activities, and sea ice and open water classification, sea ice concentration estimation has attracted significant attention due to the importance of this type of information. Satellite imagery is widely used for monitoring the ice cover. In this regard, images from synthetic aperture radar (SAR) are of interest due to their high spatial resolution. However, automated SAR imagery interpretation is a complex recognition task that requires algorithms with strong ability to learn complex features. Convolutional neural networks (CNNs) are the state-of-the-art in the image recognition field and CNNs have demonstrated an excellent ability to learn complicated image features. In this thesis, we first used a CNN-based transfer learning method to address sea ice and water classification challenge, which achieves an impressive classification accuracy (92.36%). Then sea ice concentration estimation from SAR image using CNNs is developed. The CNN models are trained from scratch using image analysis charts as ground truth. Based on the designed CNN, several studies are conducted. We first demonstrate the importance of including samples of intermediate ice concentration in our training data. Then experiments are carried out to increase the number of these samples in our dataset. The results from experiments indicate that model performance can be improved by adding more intermediate ice concentration samples from new datasets, regardless of the location, time, and sea ice features of new datasets. Another benefit of balancing the dataset is that the estimation results of intermediate ice concentrations from the CNN become more accurate. In addition, the CNN model we adopted is found to outperform other algorithms on distinguishing the marginal ice zone.

Download or read book Segmentation of RADARSAT 2 Dual polarization Sea Ice Imagery written by Peter Yu and published by . This book was released on 2009 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: The mapping of sea ice is an important task for understanding global climate and for safe shipping. Currently, sea ice maps are created by human analysts with the help of remote sensing imagery, including synthetic aperture radar (SAR) imagery. While the maps are generally correct, they can be somewhat subjective and do not have pixel-level resolution due to the time consuming nature of manual segmentation. Therefore, automated sea ice mapping algorithms such as the multivariate iterative region growing with semantics (MIRGS) sea ice image segmentation algorithm are needed. MIRGS was designed to work with one-channel single-polarization SAR imagery from the RADARSAT-1 satellite. The launch of RADARSAT-2 has made available two-channel dual-polarization SAR imagery for the purposes of sea ice mapping. Dual-polarization imagery provides more information for distinguishing ice types, and one of the channels is less sensitive to changes in the backscatter caused by the SAR incidence angle parameter. In the past, this change in backscatter due to the incidence angle was a key limitation that prevented automatic segmentation of full SAR scenes. This thesis investigates techniques to make use of the dual-polarization data in MIRGS.

Download or read book Dual polarization HH HV RADARSAT 2 ScanSAR Observations of New Young and First year Sea Ice written by John Alexander Casey and published by . This book was released on 2010 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Observations of sea ice from space are routinely used to monitor sea ice extent, concentration and type to support human marine activity and climate change studies. In this study, eight dual-polarization (dual-pol) (HH/HV) RADARSAT-2 ScanSAR images acquired over the Gulf of St. Lawrence during the winter of 2009 are analysed to determine what new or improved sea ice information is provided by dual-pol C-band synthetic aperture radar (SAR) data at wide swath widths, relative to single co-pol data. The objective of this study is to assess how dual-pol RADARSAT-2 ScanSAR data might improve operational ice charts and derived sea ice climate data records. In order to evaluate the dual-pol data, ice thickness and surface roughness measurements and optical remote sensing data were compared to backscatter signatures observed in the SAR data. The study found that: i) dual-pol data provide improved separation of ice and open water, particularly at steep incidence angles and high wind speeds; ii) the contrast between new, young and first-year (FY) ice types is reduced in the cross-pol channel; and iii) large areas of heavily deformed ice can reliably be separated from level ice in the dual-pol data, but areas of light and moderately ridged ice cannot be resolved and the thickness of heavily deformed ice cannot be determined. These results are limited to observations of new, young and FY ice types in winter conditions. From an operational perspective, the improved separation of ice and open water will increase the accuracy of ice edge and total ice concentration estimates while reducing the time required to produce image analysis charts. Further work is needed to determine if areas of heavily ridged ice can be separated from areas of heavily rafted ice based on knowledge of ice conditions in the days preceding the formation of high backscatter deformed ice. If rafted and ridged ice can be separated, tactical ridged ice information should be included on image analysis charts. The dual-pol data can also provide small improvements to ice extent and concentration data in derived climate data records. Further analysis of dual-pol RADARSAT-2 ScanSAR data over additional ice regimes and seasons is required.

Download or read book Illumination and Noise based Scene Classification Application to SAR Sea Ice Imagery written by Namrata Bandekar and published by . This book was released on 2011 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial intensity variation introduced by illumination changes is a challenging problem for image segmentation and classification. Many techniques have been proposed which focus on removing this illumination variation by estimating or modelling it. There is limited research on developing an illumination invariant classification technique which does not use any preprocessing. A major focus of this research is on automatically classifying synthetic aperture radar (SAR) images. These are large satellite images which pose many challenges for image classification including the incidence angle effect which is a strong illumination variation across the image. Mapping of full scene satellite images of sea-ice is important for navigational purposes for ships and also for climate research. The images obtained from the RADARSAT-2 satellite are dual band, high quality images. Currently, sea ice chart are produced manually by ice analysts at the Canadian Ice Service. However, this process can be automated to reduce processing time and obtain more detailed pixel-level ice maps. An automated classification algorithm to achieve sea ice and open water separation will greatly help the ice analyst by providing sufficient guidance in the initial stages of creating an ice map. It would also help the analyst to improve the accuracy while finding ice concentrations and remove subjective bias. The existing Iterative Region Growing by Semantics (IRGS) algorithm is not effective for full scene segmentation because of the incidence angle effect. This research proposes a "glocal" (global as well as local) approach to solve this problem.

Download or read book Ground truth Observations of Ice covered North Slope Lakes Imaged by Radar written by W. F. Weeks and published by . This book was released on 1981 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Arctic Sea Ice Property Retrieval from Synthetic Aperture Radar with Deep Learning Methods written by Karl Kortum and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. These techniques enable the extrapolation of sparse reference data to a large space of sea ice conditions and enable high resolution mapping of the Earth's region most affected by human-made climate change.

Download or read book Data driven Regularization and Uncertainty Estimation to Improve Sea Ice Data Assimilation written by Nazanin Asadi and published by . This book was released on 2019 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate estimates of sea ice conditions such as ice thickness and ice concentration in the ice-covered regions are critical for shipping activities, ice operations and weather forecasting. The need for this information has increased due to the recent record of decline in Arctic ice extent and thinning of the ice cover, which has resulted in more shipping activities and climate studies. Despite the extensive studies and progress to improve the quality of sea ice forecasts from prognostic models, there is still significant room for improvement. For example, ice-ocean models have difficulty estimating the ice thickness distribution accurately. To help improve model forecasts, data assimilation is used to combine observational data with model forecasts and produce more accurate estimates. The assimilation of ice thickness observations, compared to other ice parameters such as ice concentration, is still relatively unexplored since the satellite-based ice thickness observations have only recently become common. Also, preserving sharp features of ice cover, such as leads and ridges, can be difficult, due to the spatial correlations in the background error covariance matrices. At the same time, the current ice concentration assimilation systems do not directly assimilate high resolution sea ice information from synthetic aperture radar (SAR), even though they are the main source of information for operational production of ice chart products at the Canadian Ice Service. The key challenge in SAR data assimilation is automating the interpretation of SAR images. To address the problem of assimilating ice thickness observations while preserving sharp features, two different objective functions are studied. One with a conventional l2-norm and one imposing an additional l1-norm on the derivative of the ice thickness state estimate as a sparse regularization. The latter is motivated by analysis of high resolution ice thickness observations derived from an airborne electromagnetic sensor demonstrating the sparsity of the ice thickness in the derivative domain. The data fusion and data assimilation experiments are performed over a wide range of background and observation error correlation length scales. Results demonstrate the superiority of using a combined l1-l2 regularization framework especially when the background error correlation length scale was relatively short (approximately five times the analysis grid spacing). The problem of automated information retrieval from SAR images has been explored in a problem of ice/water classification. The selected classification approach takes advantage of neural networks to produce results comparable to a previous study using logistic regression. The employed dataset in both studies is a comprehensive dataset consisting of 15405 SAR images over a seven year period, covering all months and different locations. In addition, recent neural network uncertainty estimation approaches are employed to estimate the uncertainty associated with the classification of ice/water labels, which was not explored in this problem domain previously. These predicted uncertainties can improve the automated classification process by identifying regions in the predictions that should be checked manually by an analyst.

Download or read book Remote Sensing of Sea Ice Leads with Sentinel 1 C band Synthetic Aperture Radar written by Dmitrii Murashkin and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The presence of leads with open water or thin ice is an important feature of the Arctic sea ice cover. Leads regulate the heat, gas, and moisture fluxes between the ocean and atmosphere and are areas of high ice growth rates during periods of freezing conditions. In the present study an algorithm providing an automatic lead detection based on Synthetic Aperture Radar (SAR) images is developed using traditional machine learning techniques and deep learning methods. The algorithm is applied to a wide range of Sentinel-1 scenes taken over the Arctic Ocean. Distribution of the detected leads in the Arctic during winter seasons 2016--2021 is then analyzed. An important part of the algorithm development is the data preprocessing as the classification quality depends on the quality of the input images. An advanced data preparation technique improves consistency of the cross-polarization channel and enables the use of dual-polarization SAR images. By using both the HH and the HV channels instead of single co-polarized observations the algorithm is able to detect more leads compared to the use of the HH polarization only. First, a traditional machine learning approach is described. It is based on polarimetric features and texture features derived from the grey level co-occurrence matrix. The Random Forest classifier is used to investigate the individual feature importance on the lead detection. The precision-recall curve representing the quality of the classification is assessed to define a threshold for the binary lead/sea ice classification. The algorithm produces a lead classification with more than 90% precision with 60% of all leads classified, as evaluated on the test data. The precision can be increased by the cost of the amount of leads detected. Classification quality is improved by introducing an advanced binarization method based on watershed segmentation. Further improvements include object shape analysis resulting in a shape-based filter, which efficiently removes objects appearing due to noise patterns over young ice. Second, an algorithm based on a convolutional neural network is developed. It shows more robust results compared to the algorithm based on the gray level co-occurrence matrix with Random Forest classification and is applicable to the entire Arctic Ocean. Classification results are evaluated against the dataset which does not include training or testing data, and are also compared to Sentinel-2 optical satellite images. Finally, the lead detection algorithm is applied to all Sentinel-1 EW GRDM scenes taken in five winter seasons, 1 November - 30 April of 2016-2021 years. 3-day composite pan-Arctic lead maps with the native Sentinel-1 40~meters pixel spacing are produces. The frequency of lead occurrence derived from these maps is compared with MODIS thermal infrared lead detection results. The lead area fraction is compared with the AMSR2 passive microwave observations. The lead area distribution, lead length, and lead width distributions, as well as the lead orientation distributions, are analyzed in the following regions of the Arctic Ocean: Fram Strait, Barents Sea, Kara Sea, Laptev Sea, East Siberian Sea, Chukchi Sea, Beaufort Sea, Central Arctic. Each region shows the presence of regularity in lead orientation, the preferred orientation has little variation from year to year and during season. The lead width distribution is found to follow the power low with the exponent of 1.86 with 0.16 standard deviation. The yearly mean lead area fraction derived from Sentinel-1 images varies from 2.5% to 3.7% during winter seasons 2016-2021.

Download or read book On the Use of Multispectral Radar to Define Certain Characteristics of Great Lakes Ice written by S. J. Bolsenga and published by . This book was released on 1978 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Using Synthetic Aperture Radar SAR to Estimate Bathymetry and Volume of Shallow North Slope Lakes written by Celine M. Van Breukelen and published by . This book was released on 2010 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: "An efficient and cost effective method of monitoring North Slope lakes is essential for balancing the needs of industrial and environmental consumers. Arctic lakes are necessary for supporting facility and drilling operations. They are also integral parts of the Arctic ecosystem. Lakes are advantageous sites for long term monitoring of climate change. Remote sensing is a cost effective tool for sustained monitoring of this large and inaccessible environment. Synthetic Aperture Radar (SAR) was used in conjunction with the Modified Stefan's ice thickness equation to estimate bathymetry and volume of three North Slope lakes. A series C-band SAR images taken over the 2000-2001 winter were processed to differentiate between grounded and floating ice. The ice thickness of each pixel was estimated by recording the date it became grounded and the corresponding ice thickness of that date. Ice thickness was used to determine water depth, which was used to create bathymetric maps and estimate volume. The bathymetric estimates using the SAR methodology for lakes S0901, S0902 and S0903 produced an 18.08% underestimate, 19.06% underestimate and a 6.53% overestimate, when compared to ground truthed bathymetry. These results demonstrate that this method can be used for reliable, low cost evaluation of these important resources"--Leaf iii.

Download or read book River Ice Classification from High Angle Oblique Imagery Using Deep Neural Networks written by Daniel D. Hamill and published by . This book was released on 2019 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigated the use of deep convolutional neural networks (DCNN) for monitoring river ice imagery generated by remote cameras on the Pend Oreille River, Idaho. The cameras were installed at selected locations along the river to provide information on ice conditions to aid in the wintertime operation of the Albeni Falls Dam. Manually reviewing the imagery for the presence of ice is difficult for large volumes of imagery. The primary advantage of DCNNs compared to other machine learning algorithms is that features in the image can be directly inferred from the image data without any subsequent data transformations or derivative products. High-angle oblique imagery collected periodically (e.g. hourly) on the Pend Oreille River was used to repurpose a highly-efficient, pre-existing DCNN. A transfer learning methodology was employed to retrain and test the DCNN using ground training tiles derived from a weakly supervised conditional random field. The tiles represented river ice, snow, terrain, water, and vegetation. The model converged with an average testing accuracy of 95%, an average f1 score of 94%, and an ice classification accuracy of 82%. The operational effectiveness of the model was assessed by applying it to several years of winter imagery which contained a large spectrum of lighting and weather conditions. The resulting time series of predicted conditions was validated against manually sorted images. The DCNN classifier accurately predicted the daily presence of river ice with an f1 score of 76% when compared to the manual classification. The model presented in the study can aid winter operation engineering decisions of water managers by providing automated alerts of river ice conditions.

Download or read book Earth Resources written by and published by . This book was released on 1987 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: