Band Synthetic Aperture Radar Images Research Articles

SAR Image Fusion Classification Based on the Decision-Level Combination of Multi-Band Information

Synthetic aperture radar (SAR) is an active coherent microwave remote sensing system. SAR systems working in different bands have different imaging results for the same area, resulting in different advantages and limitations for SAR image classification. Therefore, to synthesize the classification information of SAR images into different bands, an SAR image fusion classification method based on the decision-level combination of multi-band information is proposed in this paper. Within the proposed method, the idea of Dempster–Shafer evidence theory is introduced to model the uncertainty of the classification result of each pixel and used to combine the classification results of multiple band SAR images. The convolutional neural network is used to classify single-band SAR images. Calculate the belief entropy of each pixel to measure the uncertainty of single-band classification, and generate the basic probability assignment function. The idea of the term frequency-inverse document frequency in natural language processing is combined with the conflict coefficient to obtain the weight of different bands. Meanwhile, the neighborhood classification of each pixel in different band sensors is considered to obtain the total weight of each band sensor, generate weighted average BPA, and obtain the final ground object classification result after fusion. The validity of the proposed method is verified in two groups of multi-band SAR image classification experiments, and the proposed method has effectively improved the accuracy compared to the modified average approach.

Remote sensing measurements of thermokarst subsidence using InSAR

AbstractThawing of ice‐rich permafrost followed by surface subsidence results in irregular, depressed landforms known as thermokarst. Many remote sensing studies have identified thermokarst landforms and mapped their changes. However, the intrinsic dynamic thermokarst process of surface subsidence remains a challenge to quantify and is seldom examined using remote sensing methods. In this study we used spaceborne interferometric synthetic aperture radar (InSAR) data to map surface subsidence trends at a thermokarst landform located near Deadhorse on the North Slope of Alaska. A pipeline access road constructed in the 1970s triggered the thawing of the permafrost, causing subsequent expansion of the thermokarst landform. Using Phased Array type L band Synthetic Aperture Radar images acquired by the Advanced Land Observing Satellite‐1, our InSAR analysis reveals localized thermokarst subsidence of 2–8 cm/yr between 2006 and 2010, equivalent to an ice volume loss of about 1.2 × 107 m3/yr. Comparisons between InSAR subsidence trends and lidar microtopography suggest a characteristic time of 8 years of thermokarst development. We also quantitatively explain the difficulty, uncertainties, and possible biases in separating thermokarst‐induced, irreversible subsidence from cyclic seasonal deformation. Our study illustrates that InSAR is an effective tool for mapping and studying active thermokarst processes and quantifying ice loss.

New building signature extraction method from single very high-resolution synthetic aperture radar images based on symmetric analysis

To monitor urban areas using a synthetic aperture radar (SAR) sensor, we propose a symmetric analysis-based building signature extraction method. Instead of using separated algorithms, a unified framework is proposed to extract both layover and shadow areas. Since these two primitives usually exhibit long strip patterns in very-high-resolution SAR images, symmetry axes are first delineated. After that, local features are extracted from both symmetry and range direction to better distinguish different primitives. Then, these local radiometric features are used to identify different categories (layover, shadow, and background) via an efficient multiclass logistic regression classifier. To discriminate individual primitives, geometric information is adopted via an improved Ramer Douglas Peucker algorithm, which also simplifies the parameters for describing these primitives. To further enhance accuracy, combinatory analysis is implemented to exclude some false detections, and then shadow areas are extended via a local region growing method. The proposed approach is tested on a 0.75-m resolution airborne C band SAR image. The experiments are carried out under both small- and large-scale scenes, and the comparative results show our method has some advantages in low-contrast target detection and false-alarm elimination.

Deformation Trend Extraction Based on Multi-Temporal InSAR in Shanghai

Shanghai is a modern metropolis characterized by high urban density and anthropogenic ground motions. Although traditional deformation monitoring methods, such as GPS and spirit leveling, are reliable to millimeter accuracy, the sparse point subsidence information makes understanding large areas difficult. Multiple temporal space-borne synthetic aperture radar interferometry is a powerful high-accuracy (sub-millimeter) remote sensing tool for monitoring slow ground deformation for a large area with a high point density. In this paper, the Interferometric Point Target Time Series Analysis method is used to extract ground subsidence rates in Shanghai based on 31 C-Band and 35 X-Band synthetic aperture radar (SAR) images obtained by Envisat and COSMO SkyMed (CSK) satellites from 2007 to 2010. A significant subsidence funnel that was detected is located in the junction place between the Yangpu and the Hongkou Districts. A t-test is formulated to judge the agreements between the subsidence results obtained by SAR and by spirit leveling. In addition, four profile lines crossing the subsidence funnel area are chosen for a comparison of ground subsidence rates, which were obtained by the two different band SAR images, and show a good agreement.

Simulating the impacts of ionospheric scintillation on L band SAR image formation

We develop a phase screen model called the Synthetic Aperture Radar (SAR) Scintillation Simulator (SAR‐SS) for predicting the impacts of ionospheric scintillation on SAR image formation. SAR‐SS consists of a phase screen generator and a propagator. The screen generator creates a 2‐D random realization of spatial phase fluctuations resulting from the traversal of small‐scale field‐aligned irregularities in the ionosphere. It accounts for the motion of the radar platform, the drift of the ionospheric irregularities, and the oblique angle of propagation, all of which determine the scale sizes of the irregularities sampled by the radar beam. The propagator solves the 3‐D parabolic wave equation using the split step technique to compute the ionospheric transfer function for two‐way propagation. This ionospheric transfer function is used to modulate the SAR signal due to terrestrial features in order to assess the ionospheric impact on SAR image formation in the small target approximation. We compare simulated and observed PALSAR imagery over Brazil during disturbed ionospheric conditions. We demonstrate that SAR‐SS can reproduce the field‐aligned streaks in PALSAR imagery caused by irregularities in the equatorial ionosphere that have been observed by previous authors. The field‐aligned streaks exhibited a dominant wavelength larger than the Fresnel break scale, which suggests that refractive scatter was dominant over diffraction as the physical mechanism responsible for the scintillation of the radar signal in this case. The spectral index of phase fluctuations in the screen was quite large (9.0), suggesting that these irregularities were possibly associated with bottomside sinudoidal irregularities rather than equatorial plasma bubbles.

Comparison between C band synthetic aperture radar and 3‐D laser scanner statistics for the Baltic Sea ice

A statistical analysis was performed for nearly simultaneously acquired C band synthetic aperture radar (SAR) images and ice freeboard statistics. The data analyzed were collected during a CryoSat calibration‐validation campaign in March 2005 in the Baltic Sea. The 3‐D ice freeboard topography along transects with a total length about 150 km and width of 300 m was constructed from cross‐track scanning airborne laser scanner measurements. The SAR image data set consisted of two nearly coincident Envisat Advanced Synthetic Aperture Radar alternating polarization precision and image mode precision images at HH polarization with their incidence angle ranges deviating about 20°. The data set represents primarily low‐salinity thin first year ice with high ice concentration and thin snow cover under cold weather conditions. The variance of the mean freeboard increased with increasing mean freeboard with the chosen window size (300 m) up to the mean freeboard of 15 cm. This multiplicative character of sea ice cover forms the basis by which we can expect that the C band backscattering coefficient response, which depends only on the ice surface roughness and the top part of ice medium, can provide information on the sea ice thickness. A nonlinear regression model with three control variables, the backscattering coefficient, the dominant thickness of the level ice which has deformed, and a variable accounting the effect of the SAR incidence angle, was established to predict the variation of the ice freeboard. The applied Bayesian approach also provided means to estimate the uncertainty range for the model fitting and the model predictions. The modeled predictions were mostly in good agreement with the measured values. The predictions for three different test lines accounted for 67%–85% of the measured freeboard variance in 300 m scale. In cold conditions with thin snow cover it was possible to estimate the degree of ice deformation and ice thickness quantitatively from C band SAR images.

Image fusion for land cover change detection

Image fusion is an effective approach for enriching multi-source remotely sensed information. In order to compensate the insufficiency of single-source remote sensing data during the change detection process, and to combine the complementary features from different sensors, this article presents the results of different temporal synthetic aperture radar (SAR) and optical image fusion algorithms for land cover change detection. First, pixel-level image fusion is performed, and its applicability for change detection is assessed by a quantitative analysis method. Second, change detection at the decision-level is put forward, which comprises object-oriented image information extraction from high-resolution optical image, multi-texture feature and support vector machines (SVM)-based information extraction from single band and single polarisation SAR image, and hard- and soft-decision based change detection. Change detection uncertainty is also evaluated at the scale of pixel using the extended probability vector and probability entropy model. The imagery used in this image fusion research was SPOT5 and RADARSAT-1 SAR data.

Eucalyptus Biomass and Volume Estimation Using Interferometric and Polarimetric SAR Data

This work aims to establish a relationship between volume and biomass with interferometric and radiometric SAR (Synthetic Aperture Radar) response from planted Eucalyptus saligna forest stands, using multi-variable regression techniques. X and P band SAR images from the airborne OrbiSAR-1 sensor, were acquired at the study area in the southeast region of Brazil. The interferometric height (Hint = difference between interferometric digital elevation model in X and P bands), contributed to the models developed due to fact that Eucalyptus forest is composed of individuals whose structure is predominantly cylindrical and vertically oriented, and whose tree heights have great correlation with volume and biomass. The volume model showed that the stand volume was highly correlated with the interferometric height logarithm (Log10Hint), since Eucalyptus tree volume has a linear relationship with the vegetation height. The biomass model showed that the combination of both Hint2 and Canopy Scattering Index—CSI (relation of s°VV by the sum of s°VV and s°HH, which represents to the canopy interaction) were used in this model, due to the fact that the Eucalyptus biomass and the trees height relationship is not linear. Both models showed a prediction error of around 10% to estimate the Eucalyptus biomass and volume, which represents a great potential to use this kind of technology to help establish Eucalyptus forest inventory for large areas.

Relation between ERS‐1 synthetic aperture radar data and measurements of surface roughness and moisture content of rocky soils in a semiarid rangeland

Surface roughness and soil moisture content control the distribution of rainfall into runoff, evapotranspiration, and infiltration. Satellite radar data have the potential to provide spatial and multitemporal estimates of these variables, depending upon the sensor configuration and field condition. The relation between the European Remote Sensing Satellite (ERS‐1) synthetic aperture radar (SAR) data and measurements of surface roughness and moisture content of rocky soils in a semiarid rangeland in southeast Arizona was analyzed in this study. A dry and a wet season C band SAR image were acquired and corrected for topographic effects. Field soil roughness and moisture content data were obtained from 47 sampling sites. An intensive soil moisture sampling campaign was also conducted at three sites to determine the number of samples necessary to estimate soil moisture content with 10% accuracy. Dry and wet season SAR data were found to be correlated (r2=0.80 and 0.59, respectively) with root‐mean‐square (RMS) height measurements, while SAR data from the wet season image were poorly correlated with soil moisture. The results indicated that C band SAR data are promising for estimation of surface roughness in semiarid rangelands. However, they are less promising for soil moisture estimation, unless the effects of soil roughness and vegetation are removed. The acquisition of an adequate number of soil moisture samples to obtain representative soil moisture measurements is also a key issue in the validation of soil moisture retrieval from SAR data. In the study area, at least 17 samples per hectare were needed to obtain soil moisture estimates with 10% accuracy.

Multifocus processing of L band synthetic aperture radar images of ocean waves obtained during the Tower Ocean Wave and Radar Dependence Experiment

As part of the Tower Ocean Wave and Radar Dependence Experiment (TOWARD) objectives, the mechanisms of SAR imaging of ocean waves are investigated using L band SAR data over the Naval Ocean Systems Center tower. This paper provides experimental evidence needed to validate the differing hypotheses. Various processing methods are investigated to generate spectra with large degrees of freedom. The results show that waves traveling in the aircraft direction are most detectable at focus settings in the range 10.0–15.0 m/s, which is consistent with the Marine Remote Sensing Experiment (MARSEN) observations reported by Jain and Shemdin (1983). Waves traveling in the direction opposite to the aircraft are most detectable at settings equal to −5.0 to −15.0 m/s. The SAR imaging system acts as a low‐pass filter with the peak of the ocean wave height spectrum occurring at higher wave numbers compared with the peak in the SAR image spectrum.

Comparison of Joint Canada‐U.S. Ocean Wave Investigation Project synthetic aperture radar data with internal wave observations and modeling results

Comparisons are made of L band and X band SAR images with in situ measurements of surface currents and surface wave spectral perturbations for two of the Joint Canada‐U.S. Ocean Wave Investigation Project internal wave data sets. Predicted and observed perturbations show agreement to within approximately a factor of 2 for L band, while the agreement at X band is much less satisfactory. Comparison of predicted microwave backscatter at X band with near‐surface radar measurements indicates large discrepancies even when surface tilt and specular reflection effects are included in the model. Synthetic aperture radar (SAR) X band modulations compare favorably with near‐surface radar measurements for range‐traveling internal waves, but for other directions, the SAR modulations are smaller than the near‐surface radar measurements. Simultaneous X and L band SAR images show comparable internal wave modulations.

Calculation of radar backscatter modulations from internal waves

Calculations of microwave backscatter from the ocean surface using the small‐perturbation method (first‐order Bragg approximation), a modified Kirchhoff approximation, and a two‐scale composite model derived from the modified Kirchhoff expression are described, and predictions of the modulation in the cross section induced by internal wave features are presented. The calculations demonstrate that the modified Kirchhoff approximation and the composite model yield equivalent cross sections if the cutoff wave number separating the long‐ and short‐wavelength regions in the composite model is chosen to be roughly one‐third the Bragg wave number. The interaction of internal waves and surface waves, assumed to modulate an equilibrium wind wave spectrum over wavelengths extending from about 0.1 to 10 m, results in significant perturbations to the L band (24 cm) and X band (3 cm) cross sections at incidence angles from 23° to 50°. These calculations indicate that the large modulations observed in X band synthetic aperture radar images of internal waves are due, at least in part, to the tilting of the X band Bragg scatterers by the internal‐wave‐modulated long‐wavelength surface waves.

Differences in radar return from ice‐covered North Slope Lakes

Comparisons are made between L and X band synthetic aperture radar images of frozen lakes on the North Slope of Alaska and ground truth observations of the nature of their ice covers. It is shown that the differences in radar backscatter observed on different areas of a lake can be correlated with whether or not the lake is frozen completely to the bottom at the site in question. This explanation is reasonable inasmuch as the reflection coefficient associated with the high‐dielectric contrast ice/water interface is significantly higher than that associated with a low‐contrast ice/soil interface. However, the presence of the ice/water interface cannot be the only condition required for the higher backscatter because the ice/water interface per se would be specular at X and L band frequencies, causing the energy returned from the interface to be reflected away from the radar receiver. The other principal factor contributing to the return of energy from the ice/water interface to the receiver is believed to be the presence in the ice of numerous vertically elongated air bubbles which would act as scatters.