Correction Methods for Non-Stationary Noise Floor in Sentinel-1 Images Using Convex Optimization

Synthetic aperture radar (SAR) is a method of creating images of the surface of the Earth by emitting and receiving radar waves. Sentinel-1 is a SAR platform made by the European Space Agency (ESA) that provides a source of SAR images open to the public through the operation of two satellites. Due to the non-uniform radiation pattern projected from the satellite's antenna, there are significant non-stationary noise floor intensity patterns that distract from the desired measurements, which are particularly significant in certain types of image modes, namely Extra Wide and Interferometric Wide modes. While ESA provides a default noise floor estimate with each Sentinel-1 product, with the intention that it be subtracted from the original image so the result is homogeneous, there is clear evidence that it is miscalibrated. This Masters thesis presents two novel methods for estimating the noise floor patterns in the images that are demonstrated to be improvements over the default noise floor. The first method presents a way to dynamically construct and apply linear rescaling to the default noise floor estimate over different sections of the images, called subswaths, by use of least squares optimization. While the method is successful in improving image quality, it is not totally effective because the default noise floor is mis-fit in a non-linear manner. The second method constructs a new noise floor as a power function of the radiation pattern power by using linear programming and least squares optimization. This successfully compensates for the non-linear mis-fit, resulting in an overall increase in image quality, albeit with greater parametric complexity. These methods greatly improve the intrinsic value of Sentinel-1 images in scenarios where the noise floor dominates, such as in cross-polarized images and images where the physical materials result in lower backscatter intensity

Oil spill and ship detection using high resolution polarimetric X-band SAR data

Among illegal human activities, marine pollution and target detection are the key concern of Maritime Security and Safety. This thesis deals with oil spill and ship detection using high resolution X-band polarimetric SAR (PolSAR). Polarimetry aims at analysing the polarization state of a wave field, in order to obtain physical information from the observed object. In this dissertation PolSAR techniques are suggested as improvement of the current State-of-the-Art of SAR marine pollution and target detection, by examining in depth Near Real Time suitability

Multitemporal Sentinel-1 and Sentinel-2 Images for Characterization and Discrimination of Young Forest Stands Under Regeneration in Norway

There is a need for mapping of forest areas with young stands under regeneration in Norway, as a basis for conducting tending, or precommercial thinning (PCT), whenever necessary. The main objective of this article is to show the potential of multitemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) data for characterization and detection of forest stands under regeneration. We identify the most powerful radar and optical features for discrimination of forest stands under regeneration versus other forest stands. A number of optical and radar features derived from multitemporal S-1 and S-2 data were used for the class separability and cross-correlation analysis. The analysis was performed on forest resource maps consisting of the forest development classes and age in two study sites from south-eastern Norway. Important features were used to train the classical random forest (RF) classification algorithm. A comparative study of performance of the algorithm was used in three cases: I) using only S-1 features, II) using only S-2 optical bands, and III) using combination of S-1 and S-2 features. RF classification results pointed to increased class discrimination when using S-1 and S-2 data in relation to S-1 or S-2 data only. The study shows that forest stands under regeneration in the height interval for PCT can be detected with a detection rate of 91% and F-1 score of 73.2% in case III as most accurate, while tree density and broadleaf fraction could be estimated with coefficient of determination (R 2 ) of about 0.70 and 0.80, respectively

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector