Models for Synthetic Aperture Radar Image Analysis

After reviewing some classical statistical hypothesis commonly used in image processing and analysis, this paper presents some models that are useful in synthetic aperture radar (SAR) image analysis

Oil Slick Characterization Using a Statistical Region-Based Classifier Applied to UAVSAR Data

During emergency responses to oil spills on the sea surface, quick detection and characterization of an oil slick is essential. The use of Synthetic Aperture Radar (SAR) in general and polarimetric SAR (PolSAR) in particular to detect and discriminate mineral oils from look-alikes is known. However, research exploring its potential to detect oil slick characteristics, e.g., thickness variations, is relatively new. Here a Multi-Source Image Processing System capable of processing optical, SAR and PolSAR data with proper statistical models was tested for the first time for oil slick characterization. An oil seep detected by NASA`s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in the Gulf of Mexico was used as a study case. This classifier uses a supervised approach to compare stochastic distances between different statistical distributions (fx) and hypothesis tests to associate confidence levels to the classification results. The classifier was able to detect zoning regions within the slick with high global accuracies and low uncertainties. Two different classes, likely associated with the thicker and thinner oil layers, were recognized. The best results, statistically equivalent, were obtained using different data formats: polarimetric, intensity pair and intensity single-channel. The presence of oceanic features in the form of oceanic fronts and internal waves created convergence zones that defined the shape, spreading and concentration of the thickest layers of oil. The statistical classifier was able to detect the thicker oil layers accumulated along these features. Identification of the relative thickness of spilled oils can increase the oil recovery efficiency, allowing better positioning of barriers and skimmers over the thickest layers. Decision makers can use this information to guide aerial surveillance, in situ oil samples collection and clean-up operations in order to minimize environmental impacts

Evaluation of Digital Classification of Polarimetric SAR Data for Iron-Mineralized Laterites Mapping in the Amazon Region

This study evaluates the potential of C- and L-band polarimetric SAR data for the discrimination of iron-mineralized laterites in the Brazilian Amazon region. The study area is the N1 plateau located on the northern border of the Carajás Mineral Province, the most important Brazilian mineral province which has numerous mineral deposits, particularly the world’s largest iron deposits. The plateau is covered by low-density savanna-type vegetation (campus rupestres) which contrasts visibly with the dense equatorial forest. The laterites are subdivided into three units: chemical crust, iron-ore duricrust, and hematite, of which only the latter two are of economic interest. Full polarimetric data from the airborne R99B sensor of the SIVAM/CENSIPAM (L-band) system and the RADARSAT-2 satellite (C-band) were evaluated. The study focused on an assessment of distinct schemes for digital classification based on decomposition theory and hybrid approach, which incorporates statistical analysis as input data derived from the target decomposition modeling. The results indicated that the polarimetric classifications presented a poor performance, with global Kappa values below 0.20. The accuracy for the identification of units of economic interest varied from 55% to 89%, albeit with high commission error values. In addition, the results using L-band were considered superior compared to C-band, which suggest that the roughness scale for laterite discrimination in the area is nearer to L than to C-band