Carl Howell

Comparison of C-band SAR algorithms to derive surface wind vectors and initial findings in their use in marine search and rescue

This paper presents a comparison of existing algorithms to derive surface winds from synthetic aperture radar (SAR) satellites and investigates their use in drift forecasting for search and rescue purposes. Specifically, SAR-derived winds from RADARSAT-1 and ENVISAT advanced synthetic aperture radar (ASAR) data at 1.5 km resolution are compared with scatterometer-derived winds. Three approaches were used to retrieve the wind vector from the SAR data, including an optimal inversion method combining SAR data and background numerical weather prediction, the geophysical model function CMOD-IFR2 with an a priori wind direction, and a technique that uses the backscatter values corresponding to two neighboring subimages with slightly different incidence angles. Our comparisons of SAR wind mapping with scatterometer winds from QuikSCAT and ERS-2 produced a root mean square error (RMSE) of 1.5 m/s. The optimal inversion method seems very promising and appears to be the best choice for assimilation of SAR-derived winds into operational wind products with respect to the datasets presented here. Additionally, the suitability of SAR imagery for search and rescue operations is reviewed. It is recommended that a method should be explored to automatically assimilate such data into operational search and rescue tools. Use of SAR winds in a search and rescue drift model is shown herein to produce improved drift trajectories on a number of search and rescue targets (e.g., life boat, sail boat, person in water).

Iceberg and ship discrimination with ENVISAT multipolarization ASAR

Spaceborne synthetic aperture radar (SAR) can provide wide area and all-weather surveillance for iceberg and ship targets. However, the discrimination between icebergs and ships in SAR imagery, especially in the single polarization imagery that has been available over the past decade, is not always reliable. This is especially true when vessel and iceberg size are on the order of the pixel spacing. Present requirements for ocean surveillance with SAR data include a high detection and classification accuracy due to the necessity of comparable performance with other reconnaissance methods, such as aerial. ENVISAT advanced SAR (ASAR) data offers a potential solution to the iceberg-ship discrimination problem. ASAR data has comparable swath and resolution to other operational SAR systems and in addition offers an alternating polarization (AP) mode. AP targets offer more information than single polarization with respect to radar scattering mechanisms. The AP ship and iceberg targets in this study were observed to have considerably different polarization responses. In particular, ship targets in the HH and HV channels were comparable. In contrast, iceberg targets had at best, weak HV responses compared to the HH channel. Two methods for target discrimination were investigated: a multipolarized area ratio and HV signal-to-clutter ratio (SCR).

A Multivariate Approach to Iceberg and Ship Classification in HH/HV ASAR Data

Spaceborne synthetic aperture radar (SAR), with its wide area coverage and all-weather operation, is an ideal sensor to provide iceberg surveillance in support of safe shipping and offshore operations. Reliable ship/iceberg discrimination in SAR imagery is at least as important as detection since misclassification can result in expending significant resources for investigation or avoidance. To address this need, the authors have undertaken research to facilitate effective target discrimination using SAR multi-polarization data. The results presented here are for iceberg and ship classification for ENVISAT advanced synthetic aperture radar (ASAR) HH/HV data. Target classification was achieved by maximizing the a posteriori probabilities obtained from Bayess rule. The maximum likelihood Gaussian classifier was used to model the probability of an unknown target belonging to either the iceberg or ship class. The feature selection algorithms, sequential forward selection (SFS), genetic algorithm (GA), and exhaustive search (ES) were evaluated for optimization of a feature space dependant multivariate classifier. The results from this study show for dual polarized HH/HV ASAR, icebergs and ships can be classified with a 93.5% accuracy using a two-class maximum likelihood model. As well, for the small sample set of 201 iceberg and ship targets presented here, suboptimal feature selection algorithms such as the SFS, GA, and exhaustive ranked search (ERS) are considered. These feature selection methods were considerably less computationally expensive to run than the global exhaustive search and were found to have converging results for both accuracy and features selected.

Dual Polarization Detection of Ships and Icebergs - Recent Results with ENVISAT ASAR and Data Simulations of RADARSAT-2

The RADARSAT-2 satellite is an advanced C-band synthetic aperture radar (SAR) with a variety of new modes including options for polarization combinations, resolution, and swath width. This paper examines the potential of multi polarization data for detecting and discriminating ship and iceberg targets Data used in this study consist of well validated airborne Convair-580 SAR and spaceborne ASAR HH/HV and HH/VV. In total, the data set used for evaluating detection and discrimination consists of 901 validated iceberg and ship targets. Optimizing target detection is accomplished using receiver operator curves (ROC) as proposed by [6] and discrimination is conducted using a quadratic discriminant (QD) with feature selection based on sequential forward selection (SFS). In general it was found that detection and discrimination improve with more polarimetric information; however, HH/HV and VV/VH only had nominally less discrimination performance than the quad polarization modes evaluated.

Pipeline encroachment monitoring using polarimetric SAR imagery

Mechanical damage incurred from unauthorized third party activities remains a leading cause of onshore oil and gas pipeline failure, indicating the need for effective strategies to monitor encroachment over extensive sections of pipeline right-of-way (ROW). In this paper, the use of polarimetric SAR imagery (as will be available from RADARSAT-2) for pipeline monitoring of encroachment activities is explored. Experimental data were acquired of a test area near the shores of Lake Simcoe (north of Toronto, Ontario) in September 2001 by the C-SAR on board the Convair-580. The vehicle deployments and ground truthing were conducted by C-CORE with processing from signal data (including calibration) and analysis performed at the Canada Centre for Remote Sensing

Short-Term Operational Sea Ice Forecasting for Arctic Shipping

The Arctic sea ice is declining in extent, volume and thickness. With this decline comes an increased interest in the two main Arctic shipping routes: Canada’s Northwest Passage (NWP) and Russian Northern Sea Route (NSR). The NWP is the most direct route between Asia and the East coast of North America. Some routes are up to 40% shorter than those using the Suez Canal.With commercial and contractual implications, Arctic shipping route access needs to be predictable with sufficient lead time to allow optimization. This paper presents a methodology for forecasting the timing and length of the open-water season (by determining freeze-up and break-up dates) on regional scales at key locations in the NWP along with examples of applications.A suite of statistical models were developed to forecast the timing and length of the open-water season at key locations within the NWP, using a multi-node based quadratic discriminant (QD) approach. Forecasts are feasible up to four weeks in advance. Ensembles of QD models were built for key regions using a feature selection method to select an optimized set of input parameters to better discriminate between two states (i.e., ice or open-water). The set of available features used included observed and modeled environmental, oceanographic and atmospheric parameters. Results of models with a 28-day forecast horizon show that over 59% of predictions for break-up and 79% of predictions for freeze-up fall within a ±4-day range, which is the error on the reference dates derived from the weekly CIS ice charts.Copyright

Integration of RADARSAT-2 Dual and Quad Polarization Data into Pipeline Third Party Encroachment Monitoring

Mechanical damage incurred from unauthorized third-party activities remains a leading cause of oil and gas pipeline failure, indicating the need for effective strategies to monitor encroachment over extensive sections of pipeline right-of-ways (ROWs). The purposes of the work discussed in this paper are to evaluate the use of polarimetric spaceborne synthetic aperture radar (SAR) for detecting vehicle targets and discriminating them from false alarms and to lay the foundation for integrating RADARSAT-2 products into the existing encroachment management system (EMS). RADARSAT-2 simulated products were created from Convair-580 imagery collected over Calgary, Canada. Results show that target detection is better for higher resolution data, but discrimination of targets from false alarms is better for dual and quad polarization data due to the increased feature set available that captures more of the scattering behavior. Overall, the Ultra Fine HH and dualpolarization Fine HH/HV or quad-polarization Fine are the recommended modes for an EMS using RADARSAT-2.

Third party encroachment monitoring using RADARSAT-1, IKONOS, EROS-A1 and simulated RADARSAT-2 imagery

Third party mechanical damage is the principal cause of on-land pipeline failure and is related to such activities as road construction, cable laying, farming and residential and commercial land development. Earth observation data with wide area coverage offers an alternative to the high cost of aerial patrol on large pipeline networks. In order to demonstrate the feasibility of using satellite imagery to detect third party encroachment upon pipeline rights-of-way, two field experiments were carried out in 2000 and 2001. These experiments were conducted at test sites in Alberta and Ontario, Canada. Satellite imagery evaluated during these trials included IKONOS and EROS-A1 optical imagery, RADARSAT-1 fine beam imagery and simulated RADARSAT-2 imagery. Both IKONOS and EROS-A1 imagery were effective in detecting a range of vehicles in areas free of cloud cover. RADARSAT-1 images were effective in the detection of larger vehicles independent of atmospheric conditions. Using simulated RADARSAT-2 it was possible to detect smaller vehicles not previously detected with RADARSAT-1 imagery.