Satellite-borne Synthetic Aperture Radar Data Research Articles

Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE

In this paper, the feasibility of satellite remote sensing in detecting and predicting locations of buried objects in the archaeological site of Saruq Al-Hadid, United Arab Emirates (UAE) was investigated. Satellite-borne synthetic aperture radar (SAR) is proposed as the main technology for this initial investigation. In fact, SAR is the only satellite-based technology able to detect buried artefacts from space, and it is expected that fine-resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (>1 m) that might be buried in the subsurface (<2 m) under optimum conditions, i.e., dry and bare soil. SAR data were complemented with very high-resolution Worldview-3 multispectral images (0.31 m panchromatic, 1.24 m VNIR) to obtain a visual assessment of the study area and its land cover features. An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. Results from SAR feature extraction and geospatial analyses showed detection of the areas on the site that were already under excavation and predicted new, hitherto unexplored archaeological areas. The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. The validated results can provide guidance for future on-site archaeological work. The pilot process developed in this work can therefore be applied to similar arid environments for the detection of archaeological features and guidance of on-site investigations.

Use of multifrequency (C‐band and L‐band) SAR data to monitor peat subsidence based on time‐series SBAS InSAR technique

AbstractPeatlands in tropical regions like Indonesia are undergoing irreversible subsidence due to changes in land use (e.g., deforestation) and land management practices (e.g., drainage alteration), resulting in massive amounts of soil carbon loss. Several satellite‐borne synthetic aperture radar (SAR) sensors are operating concurrently at different frequencies, providing potentially useful data for monitoring surface motion over tropical peatlands. This study focused on the capability of C‐band (SENTINEL‐1) and L‐band (PALSAR‐2) SAR data to monitor the surface changes in the tropical peatlands area of Bengkalis Island, Indonesia, by applying time‐series interferometric SAR (InSAR) with the small baseline subset (SBAS) technique. The average vertical velocity measured by SENTINEL‐1 and PALSAR‐2 for the period of 2018–2019 was −1.41 and −2.65 cm yr−1, respectively. We also explored the potential of groundwater level (GWL) data converted to vertical displacement for validating SBAS InSAR. PALSAR‐2 performed the best, exhibiting lower RMSE values for each land use compared to SENTINEL‐1, with an overall RMSE of 1.383 and 1.988 cm yr−1, respectively. Also, the subsidence rates of SENTINEL‐1 were underestimated, showing a significantly lower mean subsidence difference (0.96 cm yr−1) than the reference. Our GWL‐based subsidence data offered an alternative validation method for InSAR‐based subsidence estimation. Therefore, the integration of time‐series InSAR and GWL data can provide crucial information for monitoring the degradation of tropical peatlands.

Geomorphological mapping of ice-free areas using polarimetric RADARSAT-2 data on Fildes Peninsula and Ardley Island, Antarctica

Satellite-borne Synthetic Aperture Radar (SAR) has been used for characterizing and mapping in two relevant ice-free areas in the South Shetland Islands. The objective has been to identify and characterize land surface covers that mainly include periglacial and glacial landforms, using fully polarimetric SAR C band RADARSAT-2 data, on Fildes Peninsula that forms part of King George Island, and Ardley Island. Polarimetric parameters obtained from the SAR data, a selection of field based training and validation sites and a supervised classification approach, using the support vector machine were chosen to determine the spatial distribution of the different landforms. Eight periglacial and glacial landforms were characterized according to their scattering mechanisms using a set of 48 polarimetric parameters. The mapping of the most representative surface covers included colluvial deposits, stone fields and pavements, patterned ground, glacial till and rock outcrops, lakes and glacier ice. The overall accuracy of the results was estimated at 81%, a significant value when mapping areas that are within isolated regions where access is limited. Periglacial surface covers such as stone fields and pavements occupy 25% and patterned ground over 20% of the ice-free areas. These are results that form the basis for an extensive monitoring of the ice-free areas throughout the northern Antarctic Peninsula region.

Ship Detection with Spectral Analysis of Synthetic Aperture Radar: A Comparison of New and Well-Known Algorithms

The surveillance of maritime areas with remote sensing is vital for security reasons, as well as for the protection of the environment. Satellite-borne synthetic aperture radar (SAR) offers large-scale surveillance, which is not reliant on solar illumination and is rather independent of weather conditions. The main feature of vessels in SAR images is a higher backscattering compared to the sea background. This peculiarity has led to the development of several ship detectors focused on identifying anomalies in the intensity of SAR images. More recently, different approaches relying on the information kept in the spectrum of a single-look complex (SLC) SAR image were proposed. This paper is focused on two main issues. Firstly, two recently developed sub-look detectors are applied for the first time to ship detection. Secondly, new and well-known ship detection algorithms are compared in order to understand which has the best performance under certain circumstances and if the sub-look analysis improves ship detection. The comparison is done on real SAR data exploiting diversity in frequency and polarization. Specifically, the employed data consist of six RADARSAT-2 fine quad-polacquisitions over the North Sea, five TerraSAR-X HH/VV dual-polarimetric data-takes, also over the North Sea, and one ALOS-PALSAR quad-polarimetric dataset over Tokyo Bay. Simultaneously to the SAR images, validation data were collected, which include the automatic identification system (AIS) position of ships and wind speeds. The results of the analysis show that the performance of the different sub-look algorithms considered here is strongly dependent on polarization, frequency and resolution. Interestingly, these sub-look detectors are able to outperform the classical SAR intensity detector when the sea state is particularly high, leading to a strong clutter contribution. It was also observed that there are situations where the performance improvement thanks to the sub-look analysis is not so noticeable.

Using SAR images to delineate ocean oil slicks with a texture-classifying neural network algorithm (TCNNA)

Satellite-borne synthetic aperture radar (SAR) data are widely used for detection of hydrocarbon resources, pollution, and oil spills. These applications require recognition of particular spatial patterns in SAR data. We developed a texture-classifying neural network algorithm (TCNNA), which processes SAR data from a wide selection of beam modes, to extract these patterns from SAR imagery in a semisupervised procedure. Our approach uses a combination of edge-detection filters, descriptors of texture, collection information (e.g., beam mode), and environmental data, which are processed with a neural network. Examples of pattern extraction for detecting natural oil seeps in the Gulf of Mexico are provided. The TCNNA was successful at extracting targets and rapidly interpreting images collected under a wide range of environmental conditions. The results allowed us to evaluate the effects of different environmental conditions on the expressions of oil slicks detected by the SAR data. By processing hundreds of SAR images, we have also found that the optimum wind speed range to study surfactant films is from 3.5 to 7.0 m·s–1, and the best incidence angle range for surfactant detection in C-band is from 22° to 40°. Minor postprocessing supervision is required to check TCNNA output. Interpreted images produce binary arrays with imbedded georeference data that are easily stored and manipulated in geographic information system (GIS) data layers.

Applications and Analyses of Satellite‐borne L‐band Synthetic Aperture Radar Data in Coastal Environments

AbstractThere is rapid population growth in coastal environments. Development and competing demands for resources and natural hazards offer an additional threat to coastal communities. Remote sensing provides a means of monitoring the intra‐ and inter‐annual variability both natural and human impacts cause. In this article, we review the application of satellite‐borne Synthetic Aperture Radar (SAR) to the coastal zone and provide two examples, seasonal inundation mapping on coastal floodplains at the regional scale in eastern North Carolina, USA, and shoreline delineation and change in estuary of Pamlico Peninsula, Dare County, North Carolina. The results SAR data provide are reliable and repeatable, and can potentially be used to identify critical areas in coastal environments where there is a need to respond to the pressures caused by population growth, land use and land cover change, natural hazards and sea level rise that may be brought about by global climate change.

Measurement of Ocean Surface Winds Using Synthetic Aperture Radars

A methodology for retrieving high-resolution ocean surface wind fields from satellite-borne synthetic aperture radar (SAR) data is introduced and validated. The algorithms developed are suited for ocean SAR data, which were acquired at the C band of either vertical (VV) or horizontal (HH) polarization in transmission and reception. Wind directions are extracted from wind-induced streaks that are visible in SAR images of the ocean at horizontal scales greater than 200 m. These wind streaks are very well aligned with the mean surface wind direction. To extract the orientation of these streaks, two algorithms are introduced, which are applied either in the spatial or spectral domain. Ocean surface wind speeds are derived from the normalized radar cross section (NRCS) and image geometry of the calibrated SAR images, together with the local SAR-retrieved wind direction. Therefore, several C-band models (CMOD IFR2, CMOD4, and CMODS) are available, which were developed for VV polarization, and have to be extended for HH polarization. To compare the different algorithms and C-band models as well as demonstrate their applicability, SAR-retrieved wind fields are compared to numerical-model results considering advanced SAR (ASAR) data from Environmental Satellite (ENVISAT), a European satellite.

Estimating forest structure in wetlands using multitemporal SAR

Data from 202 forest plots on the Roanoke River floodplain, North Carolina were used to assess the capabilities of multitemporal radar imagery for estimating biophysical characteristics of forested wetlands. The research was designed to determine the potential for using widely available data from the current set of satellite-borne synthetic aperture radar (SAR) sensors to study forests over broad geographic areas and complex environmental gradients. The SAR data set included 11 Radarsat scenes, 2 ERS-1 images, and 1 JERS-1 scene. Empirical analyses were stratified by flood status such that sites were compared only if they exhibited common flooding characteristics. In general, the results indicate that forest properties are more accurately estimated using data from flooded areas, probably because variations in surface conditions are minimized where there is a continuous surface of standing water. Estimations yielded root mean square errors (RMSEs) for validation data around 10 m 2/ha for basal area (BA), and less than 3 m for canopy height. The r 2 values generally exceeded .65 for BA, with the best predictions coming from sample sites for which both nonflooded and flooded SAR scenes were available. The addition of early spring normalized difference vegetation index (NDVI) values from Landsat Thematic Mapper (Landsat TM) improved model predictions for BA in forests where BA levels were <55 m 2/ha. Further analyses indicated a very limited sensitivity of the individual SAR scenes to differences in forest composition, although soil properties in nonflooded areas exerted a weak but nevertheless important influence on backscatter.

Remote sensing aids in sea‐ice analysis

Satellite‐borne synthetic aperture radar (SAR) is fast becoming an indispensable tool for operational sea‐ice analysis. In anticipation of this, the Naval Research Laboratory and the National Oceanic and Atmospheric Administration established a data link between the Alaska SAR Facility (ASF) in Fairbanks and the Navy/NOAA Joint Ice Center in Suitland, Md. The Joint Ice Center is the national center for ice analysis and forecasting. At the NRL Remote Sensing Applications Branch, we are working to ensure that SAR and other satellite data are used to best advantage for ice analysis by putting together appropriate software tools for manipulating images, developing algorithms for automated image analysis, and combining sensor data in ways that increase confidence in the final product, usually an ice type and concentration map. This article gives a brief look at some recent work in each of these areas.

Concepts and techniques for real-time optical synthetic aperture radar data processing

The paper discusses the processing of synthetic-aperture-radar (SAR) data from an optical perspective, with emphasis on real-time operations. To appreciate the problem of storing and processing the large quantity of data derived from recent, and projected, satellite-borne SAR missions a brief review of optical and digital techniques is presented. It is evident that there are still severe difficulties to be overcome in achieving real-time processing of satellite-borne SAR data with present digital electronic systems. In the paper several specific optical solutions to the data processing bottleneck are examined in detail, all are based on charge coupled devices (CCDs) configured to perform multichannel processing integrated with a variety of acoustooptic architectures. These systems have the potential to rival the performance of present digital electronic systems and offer substantial reductions in size, weight and power consumption. With future increases in SAR data throughput requirements in a wide range of applications it is argued that both present and future optical systems can offer a cost-effective processing facility which may complement digital electronic computers.