Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Phase History Decomposition for Efficient Scatterer Classification in SAR Imagery

A new theory and algorithm for scatterer classification in SAR imagery is presented. The automated classification process is operationally efficient compared to existing image segmentation methods requiring human supervision. The algorithm reconstructs coarse resolution subimages from subdomains of the SAR phase history. It analyzes local peaks in the subimages to determine locations and geometric shapes of scatterers in the scene. Scatterer locations are indicated by the presence of a stable peak in all subimages for a given subaperture, while scatterer shapes are indicated by changes in pixel intensity. A new multi-peak model is developed from physical models of electromagnetic scattering to predict how pixel intensities behave for different scatterer shapes. The algorithm uses a least squares classifier to match observed pixel behavior to the model. Classification accuracy improves with increasing fractional bandwidth and is subject to the high-frequency and wide-aperture approximations of the multi-peak model. For superior computational efficiency, an integrated fast SAR imaging technique is developed to combine the coarse resolution subimages into a final SAR image having fine resolution. Finally, classification results are overlaid on the SAR image so that analysts can deduce the significance of the scatterer shape information within the image context

Constrained Tensor Decompositions for SAR Data: Agricultural Polarimetric Time Series Analysis

Tensor decompositions are a powerful tool for multidimensional data analysis, interpretation, and signal processing. This work develops a constrained tensor decomposition framework for complex multidimensional Synthetic Aperture Radar (SAR) data. The framework generalizes the Canonical Polyadic (CP) decomposition by formulating it as an optimization problem and allows precise control over the shape and properties of the output factors. The implementation supports complex tensors, automatic differentiation, different loss functions, and optimizers. We discuss the importance of constraints for physical validity, interpretability, and uniqueness of the decomposition results. To illustrate the framework, we formulate a polarimetric time series decomposition and apply it to data acquired over agricultural areas to analyze the development of four crop types at X, C, and L bands over the period of twelve weeks. The obtained temporal factors describe the changes in the crops in a compact way and show a correlation to certain crop parameters. We extend the existing polarimetric time series change analysis with the decomposition to show the changes in more detail and provide an interpretation through the polarimetric factors. The decomposition framework is extensible and promising for joint information extraction from multidimensional SAR data

Polarimetric Synthetic Aperture Radar, Principles and Application

Demonstrates the benefits of the usage of fully polarimetric synthetic aperture radar data in applications of Earth remote sensing, with educational and development purposes. Includes numerous up-to-date examples with real data from spaceborne platforms and possibility to use a software to support lecture practicals. Reviews theoretical principles in an intuitive way for each application topic. Covers in depth five application domains (forests, agriculture, cryosphere, urban, and oceans), with reference also to hazard monitorin

RADARSAT-2 Polarimetric Radar Imaging for Lake Ice Mapping

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

Evaluation of Multi-frequency Synthetic Aperture Radar for Subsurface Archaeological Prospection in Arid Environments

The discovery of the subsurface paleochannels in the Saharan Desert with the 1981 Shuttle Imaging Radar (SIR-A) sensor was hugely significant in the field of synthetic aperture radar (SAR) remote sensing. Although previous studies had indicated the ability of microwaves to penetrate the earth’s surface in arid environments, this was the first applicable instance of subsurface imaging using a spaceborne sensor. And the discovery of the ‘radar rivers’ with associated archaeological evidence in this inhospitable environment proved the existence of an earlier less arid paleoclimate that supported past populations. Since the 1980’s SAR subsurface prospection in arid environments has progressed, albeit primarily in the fields of hydrology and geology, with archaeology being investigated to a lesser extent. Currently there is a lack of standardised methods for data acquisition and processing regarding subsurface imaging, difficulties in image interpretation and insufficient supporting quantitative verification. These barriers keep SAR technology from becoming as integral as other remote sensing techniques in archaeological practice The main objective of this thesis is to undertake a multi-frequency SAR analysis across different site types in arid landscapes to evaluate and enhance techniques for analysing SAR within the context of archaeological subsurface prospection. The analysis and associated fieldwork aim to address the gap in the literature regarding field verification of SAR image interpretation and contribute to the understanding of SAR microwave penetration in arid environments. The results presented in this thesis demonstrate successful subsurface imaging of subtle feature(s) at the site of ‘Uqdat al-Bakrah, Oman with X-band data. Because shorter wavelengths are often ignored due to their limited penetration depths as compared to the C-band or L-band data, the effectiveness of X-band sensors in archaeological prospection at this site is significant. In addition, the associated ground penetrating radar and excavation fieldwork undertaken at ‘Uqdat al-Bakrah confirm the image interpretation and support the quantitative information regarding microwave penetration

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa

Proceedings of the Third Spaceborne Imaging Radar Symposium

This publication contains summaries of the papers presented at the Third Spaceborne Imaging Radar Symposium held at the Jet Propulsion Laboratory (JPL), California Institute of Technology, in Pasadena, California, on 18-21 Jan. 1993. The purpose of the symposium was to present an overview of recent developments in the different scientific and technological fields related to spaceborne imaging radars and to present future international plans. This symposium is the third in a series of 'Spaceborne Imaging Radar' symposia held at JPL. The first symposium was held in Jan. 1983 and the second in 1986

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin