Synthetic Aperture Radar Applications Research Articles

Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

Oil Spill Discrimination by Using General Compact Polarimetric SAR Features

Ocean surveillance is one of the important applications of synthetic aperture radar (SAR). Polarimetric SAR provides multi-channel information and shows great potential for monitoring ocean dynamic environments. Oil spills are a form of pollution that can seriously affect the marine ecosystem. Dual-polarimetric SAR systems are usually used for routine ocean surface monitoring. The hybrid dual-pol SAR imaging mode, known as compact polarimetry, can provide more information than the conventional dual-pol imaging modes. However, backscatter measurements of the hybrid dual-pol mode depend on the transmit wave polarization, which results in lacking consistent interpretation for various compact polarimetric (CP) images. In this study, we will explore the capability of different CP modes for oil spill detection and discrimination. Firstly, we introduce the general CP formalism method to formulate an arbitrary CP backscattered wave, such that the target scattering vector is characterized in the same framework for all CP modes. Then, a recently proposed CP decomposition method is investigated to reveal the backscattering properties of oil spills and their look-alikes. Both intensity and polarimetric features are studied to analyze the optimal CP mode for oil spill observation. Spaceborne polarimetric SAR data sets collected over natural oil slicks and experimental biogenic slicks are used to demonstrate the capability of the general CP mode for ocean surface surveillance.

Ground Moving Target Imaging Based on Compressive Sensing Framework With Single-Channel SAR

Ground moving target imaging (GMTIm) is considered one of the most important applications of synthetic aperture radar (SAR). Phase modulation from a moving target's higher-order movements severely degrades the focusing quality of SAR images, because the conventional SAR-GMTIm algorithm assumes a constant target velocity in high-resolution GMTIm with single channel SAR. To solve this problem, a novel SAR-GMTIm algorithm in the compressive sensing (CS) framework is proposed to obtain high-resolution SAR images with highly focused responses and accurate relocation. After taking direct action on data in the defocused region of interest (ROI) from the entire image scene, CS theory is used to decompose an SAR-GMTIm signal into a set of the polynomial basis functions to remove the various phase errors related to higherorder movements. A modified orthogonal matching pursuit (MOMP)-type basis function-searching scheme is adopted to determine the motion parameter and reconstruct the sensing dictionary matrix. We can generate a refocused image of SAR-GMTIm from the complete SAR-GMTIm signal recovered using the proposed method. Finally, simulated and real measured SAR data are used to validate the effectiveness and superiority of the proposed method for SAR-GMTIm.

Velocity estimation of rock-fall in multi-channel SAR system with low PRF sampling

Rockfalls influence the safety of the surrounding infrastructure and the normal operation of transportation lines. The application of synthetic aperture radar (SAR) technology is a new method of rock-fall monitoring and disaster prediction. This paper introduces a novel method to velocity estimation of rock-fall target utilizing SAR with low pulse repetition frequency (PRF) sampling. As is known to us, when PRF is less than the Doppler spectrum bandwidth, rock-fall target suffer both Doppler centroid frequency ambiguity and Doppler spectrum ambiguity. Under this condition, the traditional velocity estimation method in the Doppler domain is out of action. To solve above problem, this paper designs an improved reconstruction filter bank to reconstruct the Doppler spectrum of rock-fall target, and then Doppler in-band energy and Doppler out-of-band energy ratio of rock-fall target is utilized to estimate the velocity of rock-fall target. Simulation experiment results show the effectiveness of proposed method.

Development and Performance Evaluation of Small SAR System for 100-kg Class Satellite

This article shows development and performance evaluation of a MicroX-SAR demonstration satellite, scheduled to be launched in 2020 for synthetic aperture radar (SAR) application. This compact SAR system and its instruments are briefly described. The SAR antenna consists of deployable seven panels with a slot array and the antenna size is 4.9 × 0.7 m. This article proposes a simple evaluation method of the SAR antenna. The seven-panel antenna array performance is estimated from isolated single-panel antenna measurement results and synthesized scattering matrix. It is validated by comparing with near-field measurement of a seven-panel antenna array. The on-board SAR system is combined with a single-point source simulator and the point source SAR image is evaluated. The ambiguity to the signal ratio is computed for the bandwidth-integrated seven-panel antenna radiation patterns, and finally the pulse repetition frequency selection is computed along with noise equivalent sigma zero to estimate the SAR image quality based on various requirements of swath-width and ground-range resolution as per the application of interest. The prototype system is capable of 1-m ground-range resolution SAR image acquisition for 28-km swath-width within acceptable limits of signal-to-noise ratio and ambiguity levels.

Indoor THz SAR Trajectory Deviations Effects and Compensation With Passive Sub-mm Localization System

In radar remote sensing, the Terahertz (THz) spectrum is presently being investigated worldwide with focus on short-range indoor and outdoor applications. The spectrum broadens the unmanned aerial vehicle (UAV) based synthetic aperture radar (SAR) applications to indoor room profiling with sub-mm resolution and material characterization as many materials have unique fingerprints at this spectrum. SAR technique requires precise localization information of the mobile radar sensor, which in conventional SAR is achieved using an existing localization infrastructure, such as a global positioning system (GPS) and inertial measurement unit (IMU). For the indoor THz SAR, the GPS does not provide coverage in indoor complex environments, and also the state-of-art compact IMU does not provide the required sub-mm accuracy. These limitations can be overcome by utilizing an indoor localization system. Therefore, this paper presents an indoor THz simultaneous localization and mapping (SLAM) system. The system comprises of passive tags based radio frequency identification (RFID) localization system and SAR that provides the UAV localization and mapping of the in-room objects. Another challenge for the UAV based indoor THz SAR that is addressed in this paper is motion compensation (MOCO). At the THz, MOCO requires special consideration due to very small trajectory deviation is in the range of carrier wavelength. Therefore, to study the effects of the sub-mm translational errors, a testbed has been set up, and measurement results are presented in this paper along with the 3D electromagnetic simulation results for a carrier frequency of 275 GHz and bandwidth of 50 GHz. Further, to compensate these errors, the sub-mm localization system is used and the results are presented to validate the proposed solution for indoor THz SAR MOCO.

A Modification to Time-Series Coregistration for Sentinel-1 TOPS Data

Very high requirement of co-registration accuracy better than 0.001 pixels for Sentinel-1 TOPS (Terrain Observation by Progressive Scan) mode presents a great challenge for application of SAR (synthetic aperture radar) interferometry in Earth Observation. The state-of-the-art techniques have demonstrated that the low coherence scenarios and abrupt loss of coherence between two arbitrary acquisitions are main sources of error to degrade the performance of TOPS time-series co-registration. In this paper, we present a modification to overcome both limitations through the coherence enhancement. The motive behind this is to improve the quality of observations before co-registration and meanwhile avoiding the coherence loss caused by fast decorrelation. To this end, principal components analysis based spatio-temporal filtering is first used to remove the artifacts in burst interferograms over strong noise areas. Rather than heuristically choosing a sub-set of interferograms as a small baseline technique does, we use Dijkstra's shortest path algorithm under graph theory framework to maximize the coherence of a sub-set interferograms. The performance of presented method against the state-of-the-art techniques is fully evaluated by synthetic data and a Sentinel-1A stack over a low coherence scene in Indonesia. Comprehensive comparisons demonstrate 9%–17% uncertainty reduction of time-series co-registration when applying our method.

Monitoring and Detecting Archaeological Features with Multi-Frequency Polarimetric Analysis

In remote sensing for archaeology, an unequivocal method capable of automatic detection of archaeological features still does not exists. Applications of Synthetic Aperture Radar (SAR) remote sensing for archaeology mainly focus on high spatial resolution SAR sensors, which allow the recognition of structures of small dimension and give information of the surface topography of sites. In this study we investigated the potential of combined dual and fully polarized SAR data and performed polarimetric multi-frequency and multi-incidence angle analysis of C-band Sentinel-1, L-band Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) and of C-band Radar Satellite-2 (RADARSAT-2) datasets for the detection of surface and subsurface archaeological structures over the United Nations Educational, Scientific and Cultural Organization (UNESCO) site of Gebel Barkal (Sudan). While PALSAR offers a good historical reference, Sentinel-1 time series provide recent and systematic monitoring opportunities. RADARSAT-2 polarimetric data have been specifically acquired in 2012/2013, and have been scheduled to achieve a multi-temporal observation of the archaeological area under study. This work demonstrated how to exploit a complex but significant dataset composed of SAR full polarimetric and dual polarimetric acquisitions, with the purpose of identifying the most suitable earth observation technique for the preservation and identification of archaeological features. The scientific potential of the illustrated analysis fits perfectly with the current delicate needs of cultural heritage; such analysis demonstrates how multi-temporal and multi-data cultural heritage monitoring can be applied not only for documentation purposes, but can be addressed especially to those areas exposed to threats of different nature that require a constant and prompt intervention plans.

The Integrated Simulation and Processing Tool for Ground Based Synthetic Aperture Radar (GBSAR)

Ground-based Synthetic Aperture Radar (GBSAR) is a tremendous example of the extended applications of Synthetic Aperture Radar (SAR). GBSAR is extremely useful in human-made structure observations, terrain mapping, landslide monitoring and many more. However, the process of designing and developing the GBSAR system is rather costly and time-consuming. It would be of a great advantage for system designers to have a realistic simulation and designing tool to anticipate the results before the implementation of the final design. In this paper, we are going to present the integrated simulation and designing tool that we have developed for a generic GBSAR system. We named it iSIM v2.0

The Integrated Simulation and Processing Tool for Ground Based Synthetic Aperture Radar (GBSAR)

Ground-based Synthetic Aperture Radar (GBSAR) is a tremendous example of the extended applications of Synthetic Aperture Radar (SAR). GBSAR is extremely useful in human-made structure observations, terrain mapping, landslide monitoring and many more. However, the process of designing and developing the GBSAR system is rather costly and time-consuming. It would be of a great advantage for system designers to have a realistic simulation and designing tool to anticipate the results before the implementation of the final design. In this paper, we are going to present the integrated simulation and designing tool that we have developed for a generic GBSAR system. We named it iSIM v2.0.

A novel biologically-inspired target detection method based on saliency analysis for synthetic aperture radar (SAR) imagery

Saliency Object Detection (SOD) models driven by the biologically-inspired Focus of Attention (FOA) mechanism can result in highly accurate saliency maps. However, their application in high-resolution Synthetic Aperture Radar (SAR) images entails a number of intractable problems due to complex backgrounds. In this paper, we propose a novel hierarchical self-diffusion saliency (HSDS) method for detecting vehicle targets in large scale SAR images. To reduce the influence of cluttered returns on saliency analysis, we learn a weight vector from the training set to capture optimal initial saliency of the superpixels during saliency diffusion. By accounting for the multiple sizes of background objects, the saliency analysis is implemented in multi-scale space, and a saliency fusion strategy employed to integrate the multi-scale saliency maps. Simulation experiments demonstrate that our proposed method can produce a more accurate and stable detection performance, with decreased false alarms, compared to benchmark approaches.

Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data

The quantification of vegetation height for the circumpolar Arctic tundra biome is of interest for a wide range of applications, including biomass and habitat studies as well as permafrost modelling in the context of climate change. To date, only indices from multispectral data have been used in these environments to address biomass and vegetation changes over time. The retrieval of vegetation height itself has not been attempted so far over larger areas. Synthetic Aperture Radar (SAR) holds promise for canopy modeling over large extents, but the high variability of near-surface soil moisture during the snow-free season is a major challenge for application of SAR in tundra for such a purpose. We hypothesized that tundra vegetation height can be derived from multispectral indices as well as from C-band SAR data acquired in winter (close to zero liquid water content). To test our hypothesis, we used C-band SAR data from Sentinel-1 and multi-spectral data from Sentinel-2. Results show that vegetation height can be derived with an RMSE of 44 cm from Normalized Difference Vegetation Index (NDVI) and 54 cm from Tasseled Cap Wetness index (TC). Retrieval from C-band SAR shows similar performance, but C-VV is more suitable than C-HH to derive vegetation height (RMSEs of 48 and 56 cm respectively). An exponential relationship with in situ height was evident for all tested parameters (NDVI, TC, C-VV and C-HH) suggesting that the C-band SAR and multi-spectral approaches possess similar capabilities including tundra biomass retrieval. Errors might occur in specific settings as a result of high surface roughness, high photosynthetic activity in wetlands or high snow density. We therefore introduce a method for combined use of Sentinel-1 and Sentinel-2 to address the ambiguities related to Arctic wetlands and barren rockfields. Snow-related deviations occur within tundra fire scars in permafrost areas in the case of C-VV use. The impact decreases with age of the fire scar, following permafrost and vegetation recovery. The evaluation of masked C-VV retrievals across different regions, tundra types and sources (in situ and circumpolar vegetation community classification from satellite data) suggests pan-Arctic applicability to map current conditions for heights up to 160 cm. The presented methodology will allow for new applications and provide advanced insight into changing environmental conditions in the Arctic.

Evaluation of Sentinel-3 SRAL SAR altimetry over Chinese rivers

Satellite radar altimetry observations of water surface elevation (WSE) have become an important data source to supplement river gauge records. Sentinel-3 is the first radar altimetry mission operating with a synthetic aperture radar (SAR) altimeter at global scale and with a new on-board tracking system (i.e. open-loop), which has great potential in terms of delivering reliable observations of inland water bodies for the next two decades (several future missions include an open-loop tracking mode). In this context, it is very important to investigate the data quality at an early stage. In this study, a comprehensive evaluation of Sentinel-3A (S3A) is conducted at 50 virtual stations (VS) located on a wide range of rivers in China.The evaluation of Level 1 data shows that, over mountain rivers, a good prior surface elevation estimate on-board is vital to deliver useful datasets using the S3A open-loop tracking system. The Open-Loop Tracking Command version 5 (OLTC V5) has significantly improved the placement of the range window, which was misplaced and resulted in lack of data over many mountain rivers prior to OLTC V5 (March 2019). However, application of S3A over mountain rivers still requires careful evaluation, especially before March 2019.Four retrackers are evaluated including a physical SAR Altimetry Mode Studies and Applications retracker (SAMOSA+), a traditional Offset Center Of Gravity (OCOG), a Primary Peak Center Of Gravity (PPCOG), and a modified Multiple Waveform Persistent Peak (MWaPP+) retracker. For 26 VSs in plain areas, retracked WSE data achieved a root mean square error (RMSE) ranging from 0.12 m to 0.9 m. The comparison of retracking methods reveals that SAMOSA+, OCOG, and PPCOG are unable to handle multi-peak waveforms. But the MWaPP+ can significantly improve the accuracy of the estimated WSE over large rivers, especially when the waveforms are contaminated.Moreover, our result shows no considerable difference between medium (ca. 300 m wide) and large (wider than 500 m) rivers. Instead, surrounding topography and homogeneity of surroundings are very important factors influencing the shape of a waveform. For rivers surrounded by lakes, man-made channels etc., special care must be taken when processing altimetry data. Dedicated retracking methods, such as MWaPP+, and sophisticated methods for outlier detection are needed to improve precision over such rivers, as demonstrated here for the Yangtze River.

Analysis of the applicability of multi-temporal full polarimetric airborne L-band SAR scattering to paddy rice field mapping

ABSTRACTA Synthetic Aperture Radar (SAR) is an all-weather imaging system that is often used for mapping paddy rice fields and estimating the area. Fully polarimetric SAR is used to detect the microwave scattering property. In this study, a simple threshold analysis of fully polarimetric L-band SAR data was conducted to distinguish paddy rice fields from soybean and other fields. We analysed a set of ten airborne SAR L-band 2 (Pi-SAR-L2) images obtained during the paddy rice growing season (in June, August, and September) from 2012 to 2014 using polarimetric decomposition. Vector data for agricultural land use areas were overlaid on the analysed images and the mean value for each agricultural parcel computed. By quantitatively comparing our data with a reference dataset generated from optical sensor images, effective polarimetric parameters and the ideal observation season were revealed. Double bounce scattering and surface scattering component ratios, derived using a four-component decomposition algorithm, were key to extracting paddy rice fields when the plant stems are vertical with respect to the ground. The alpha angle was also an effective factor for extracting rice fields from an agricultural area. The data obtained during August show maximum agreement with the reference dataset of estimated paddy rice field areas.

Oceanic Application of Satellite Synthetic Aperture Radar

Oceanic Application of Satellite Synthetic Aperture Radar

A novel microstrip antenna with L‐shaped slots for circularly polarized satellite applications

AbstractA novel compact design of a circularly polarized corner‐slotted microstrip antenna for satellite and Synthetic Aperture Radar applications is presented. A single probe feed is used to excite a single square patch with two L‐shaped slots added at the upper right corner and at the opposite lower left corner (or at the other two corners) to achieve circular polarization (CP) radiation. The proposed antenna has a simple shape, straightforward fabrication, and low cost. The impedance bandwidth (10 dB return loss) is approximately 600 MHz at 8.1 GHz. An axial ratio bandwidth of 170 MHz is realized. The importance of the axial ratio 3 dB bandwidth is emphasized. In addition, an analysis for the direction of rotation of the CP is given. The experimental results have a reasonable agreement with the simulation.

Microstrip antenna with L‐shaped slots for circularly polarised satellite applications

A novel compact design of a circularly polarised corner-slotted microstrip antenna for satellite and synthetic aperture radar (SAR) applications is presented. A single-feed is used to excite a single-square patch with two L-shaped slots added at the upper right corner and at the opposite lower left corner (or at the other two corners) to achieve circular polarisation (CP) radiation. The proposed antenna has a simple shape, straightforward fabrication, and low cost. The impedance bandwidth (10 dB return loss) is ∼600 MHz at 8.1 GHz. An axial ratio bandwidth of 170 MHz is realised. The importance of the axial ratio 3 dB bandwidth is emphasised. In addition, an analysis for the direction of rotation of the CP is given. The experimental results have a reasonable agreement with the simulation.

ESTIMATING CANOLA’S BIOPHYSICAL PARAMETERS FROM TEMPORAL, SPECTRAL, AND POLARIMETRIC IMAGERY USING MACHINE LEARNING APPROACHES

Abstract. The objective of this study was to investigate the application of multi-temporal optical and polarimetric synthetic aperture radar (PolSAR) Earth observations for crop characterization. Crop dry biomass, Leaf Area Index (LAI), and Plant Water Content (PWC) were estimated and assessed using Machin learning approaches. An accurate estimation of crop parameters provides essential information to increased food production and plays a crucial role in the management of agricultural lands. Multispectral and PolSAR data provide valuable observations of spectral and structural properties which are essential for crops parameter modelling. The Earth observations used in this paper were collected by RapidEye satellites and Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system in the summer of 2012, over an agriculture area in Winnipeg, Manitoba, Canada. The RapidEye vegetation indices (VIs) and UAVSAR polarimetric parameters were used as inputs in artificial neural network (ANN) and support vector regression (SVR) models for canola biophysical parameters estimation. The best models were provided by SVR for canola. Also combining optical VIs and polarimetric features appeared as a powerful tool for crop parameters estimation in agricultural lands.

FULL POLARIMETRIC UAVSAR IMAGE CHANGE DETECTION BASED ON CHANGE INDICES

Abstract. Change detection is one of the most important applications of Polarimetric Synthetic Aperture Radar (PolSAR) data in monitoring urban development and supporting urban planning due to the sensibility of SAR signal to geometrical and physical properties of terrestrial features. In this paper, we proposed an unsupervised change detection method using change indices extracted from PolSAR images. Kernel k-means clustering was then performed to extract changed areas. The kernel k-means clustering is an unsupervised algorithm that maps the input features to higher Hilbert dimension space by using a kernel function. To better representation of changed areas, different change indices were generated. The method was applied to UAVSAR L-band SAR images acquired over an urban area in San Andreas, United States. We evaluated the change detection performance based on kappa and overall accuracies of the proposed approach and compared with other well-known classic methods.