Pradeep Bobby

Well site extraction from Landsat-5 TM imagery using an object-and pixel-based image analysis method

Well sites, including both well pads and exploratory core holes, are small polygonal landscape disturbance features approximately one half to one hectare (0.5–1 ha) in area, resulting from oil and gas exploration activities. Automatic extraction and monitoring of such small features using remote-sensing technology at regional scales has always been desirable for wildlife habitat monitoring and environmental planning and modelling. Due to the vast disturbances of well sites in a province like Alberta, Canada, high-resolution imagery is not practical for well site extraction. For operational purposes, mid-resolution and cost-effective satellite imagery such as Landsat is the choice. However, automatic well site extraction using mid-resolution satellite imagery is a challenging task. Wells are typically less than three pixels in width and length in a Landsat multispectral image. Furthermore, the spectral contrast between the well site pixels and the surrounding areas is low due to vegetation regrowth and the spectral complexity of the surrounding environment. This article presents a novel methodology for automatic extraction of well sites from Landsat-5 TM imagery. The method combines both pixel- and object-based image analyses and contains three major steps: geometric enhancement, segmentation, and well site extraction. The method was applied to Landsat-5 TM images acquired over Fort McMurray, Alberta, Canada. For accuracy assessment, four regions of interest were selected and the results of the proposed automatic method were evaluated against visual inspection of the Landsat-8 pan-sharpened image. The method results in a total average correctness, completeness, and quality measures of about 80, 96, and 77%, respectively over the four sites. In addition, the method is very fast as an entire Landsat scene is processed in less than 10 minutes. The method is an operational approach for automatic detection of well sites over the entire province and can dramatically reduce the labour cost of manual digitization for monitoring and updating well site maps.

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.

Detection and characterization of extreme ice features in single high resolution satellite imagery

Information on the locations and characteristics of extreme sea ice features (such as, hummocks, ridges, stamukhas and icebergs) is important for various marine applications. Imagery acquired by high resolution optical satellites was previously used for qualitative image interpretation to identify various sea ice features and it is especially valuable when detailed ground validation is not available. Current optical satellites, such as GeoEye-1, are able to acquire images with very high resolution of 0.5m. This work addresses the problem of quantitative retrieval of ice feature parameters from very high resolution optical imagery. The developed algorithms facilitate extraction of ice feature height from shadow and derivation of statistical information on ice deformation parameters. Automated processing of GeoEye-1 image demonstrated capabilities of retrieval of ridge frequency and segmentation of rubble fields.

Monitoring extreme ice features using multi-resolution RADARSAT-2 data

Information on extreme ice features (ridges, icebergs etc.) is important for various marine operations. Satellite synthetic aperture radar (SAR) imagery is capable of monitoring sea ice, identifying and tracking ice features over broad spatial scales. This work investigates possibilities of extreme sea ice features retrieval from various RADARSAT-2 data. Several different beam modes and analysis techniques, such as polarimetric decompositions, were investigated. It was demonstrated that the spatial frequency of sea ice ridges has a very good correlation with the SAR backscatter coefficient. The problem of discriminating glacier ice from sea ice can be resolved by applying Pauli decomposition to full polarimetric data.

A pixel- and object-based image analysis framework for automatic well site extraction at regional scales using Landsat data

Development associated with oil and gas exploration has expanded rapidly in Alberta and Northwest Territories, Canada. Such explorations result in landscape disturbances including forest cuts, seismic lines, well and waste sites. This paper describes a novel methodology for automatic extraction of well sites from Landsat-5 TM imagery. The method combines pixel-based and object-based image analyses and contains three major steps: geometric enhancement, segmentation, and well site extraction. For accuracy assessment, a small part of the image was used and the results were compared against visual counting of well sites visible in the pan-sharpened image of Landsat-8 of the same area. Results show correctness, completeness and quality factors of 87.3%, 96.2%, and 83.7%, respectively.

Simulating SCN and MSSR modes of RADARSAT-2 for ship and iceberg discrimination

A tool developed for simulating RADARSAT-2 (RS2) Maritime Satellite Surveillance Radar (MSSR) mode data from higher resolution data is described. RS2 Fine and Fine Quad images containing validated ship and iceberg targets were resampled to low resolution ScanSAR Narrow (SCN) and MSSR mode data. This tool can be adopted to use other image modes as inputs and simulate other outputs as well and the simulated products are used to develop a ship and iceberg discriminator for those modes. A series of tests were applied to verify the accuracy of the backscatter characteristics of the simulated products and the performance of the target discriminator are presented for SCN and MSSR mode Ocean Surveillance, Very wide swath, Near incidence (OSVN)[1]. Since there was a very limited ship data suitable for simulating MSSR mode available, only a demonstration of MSSR OSVN classifier was included.

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.