Statistical tests for a ship detector based on the Polarimetric Notch Filter

Ship detection is an important topic in remote sensing and Synthetic Aperture Radar has a valuable contribution, allowing detection at night time and with almost any weather conditions. Additionally, polarimetry can play a significant role considering its capability to discriminate between different targets. Recently, a new ship detector exploiting polarimetric information was developed, namely the Geometrical Perturbation Polarimetric Notch Filter (GP-PNF). This work is focused on devising two statistical tests for the GP-PNF. The latter allow an automatic and adaptive selection of the detector threshold. Initially, the probability density function (pdf) of the detector is analytically derived. Finally, the Neyman-Pearson (NP) lemma is exploited to set the threshold calculating probabilities using the clutter pdf (i.e. a Constant False Alarm Rate, CFAR) and a likelihood ratio (LR). The goodness of fit of the clutter pdf is tested with four real SAR datasets acquired by the RADARSAT-2 and the TanDEM-X satellites. The former images are quad-polarimetric, while the latter are dual-polarimetric HH/VV. The data are accompanied by the Automatic Identification System (AIS) location of vessels, which facilitates the validation of the detection masks. It can be observed that the pdf's fit the data histograms and they pass the two sample Kolmogorov-Smirnov and χ2 tests

A change detector based on an optimization with polarimetric SAR imagery

The possibility to detect changes in land cover with remote sensing is particularly valuable considering the current availability of long time series of data. SAR can play an important role in this context, since it can acquire complete time series without limitations of cloud cover. Additionally, polarimetry has the potential to improve significantly the detection capability allowing the discrimination between different polarimetric targets. This paper is focused on developing two new methodologies for testing the stability of observed targets (i.e. Equi-Scattering Mechanisms hypothesis) and change detection. Both the algorithms adopt a Lagrange optimization, which can be performed with two eigen-problems. Interestingly, the two optimizations share the same eigenvectors. Three statistical tests are proposed to set the threshold for the change detector. Two of them are mostly aimed at point targets and one is more suited for distributed targets. All the algorithms and procedures developed in this paper are tested on two different quad-polarimetric dataset acquired by the E-SAR DLR system in L-band (SARTOM 2006 and AGRISAR 2006 campaigns). The dataset are accompanied by ground surveys. The detectors are able to identify targets and areas with validated changes or showing clear differences in the images. The theoretical pdf exploited to model the optimum ratio fits adequately the data and therefore has been used for the statistical tests. Regarding the output of the tests, two of them provided good results, while one needs more care and adjustments

Biomass estimation as a function of vertical forest structure and forest height. Potential and limiations for remote sensing (radar and LiDAR)

Forest biomass stock, spatial distribution and dynamics are unknown parameters for many regions of the world. Today’s information is largely based on ground measurements on a plot basis without coverage in many remote regions that are fundamental for the global carbon cycle. Thus, a method capable of quantifying biomass by means of Remote Sensing (RS) could help to reduce these uncertainties and contribute to a better understanding of it. In this study the capacity to improve the estimation of above-ground biomass (AGB) with a new approach based on forest vertical structure and its potential to improve RS estimations is analyzed. Height to biomass allometry allows biomass estimations from remote sensing systems capable to resolve forest height (LiDAR and polarimetric SAR interferometry (Pol-InSAR)). However, this approach meets its limitations for forest ecosystems under changing conditions in density and structure. To improve biomass estimation accuracy, additional parameters need to be measured. Pol-InSAR and LiDAR allow getting besides forest height vertical backscattering profiles which are connected to forest vertical structure. Thus, due to the relation between structural parameters and AGB expressed by the Structure to Biomass allometry, AGB can be potentially inverted from these systems. The best characterization of forest vertical structure is obtained using the Legendre polynomials. Biomass profiles can be then characterized by the decomposition into a set of Legendre-Fourier basis functions. This method is able to accurately reconstruct vertical biomass profiles with low frequency features. Vertical backscattering profiles are strongly dependent on the sensor used as the resulting profiles are very sensitive to the wavelength and system geometry. E.g. LiDAR profiles are more sensitive to leaves and crowns while Pol-InSAR tends to reconstruct more the woody compartments (stems and branches). In this study, vertical backscattering profiles from short footprint airborne LiDAR and Pol-InSAR data are evaluated for their potential to reconstruct vertical forest structure. With the Legendre decomposition it is possible to parameterize the vertical backscattering profiles and relate them to forest biomass; even though for each remote sensing system different calibration methodologies must be derived. A first step is achieved using the calibration of backscattering signal with known biomass levels showing optimum results. In order to reduce the need of known parameters a new calibration methodology that exploits height to biomass allometric relations has been derived. Inversions using this methodology are tested for LiDAR and SAR profiles showing good correlations for an optimum subset of samples. As each system (frequency) is sensitive to certain biomass components an underestimation is generally expected. Research in this area is ongoing and will be presented with special focus on each system capacity to reconstruct forest vertical biomass distribution for broader sets of samples

Quantifying Temporal Decorrelation over Boreal Forest at L- and P-band

Temporal decorrelation is probably the most critical factor towards a successful implementation of Pol-InSAR parameter inversion techniques in terms of repeat-pass InSAR scenarios. In this paper the effect and impact of temporal decorrelation at L- and P-band is quantified. For this, data acquired by DLR’s E-SAR system in the frame of the BioSAR campaign (initiated and sponsored by the European Space Agency (ESA)) over boreal forest with variable temporal baseline in 2007 in Sweden are analyzed. For validation lidar data and ground measurements data are used

3-D glacier subsurface characterization using SAR polarimetry

The paper introduces a new polarimetric scattering model able to interpret and invert coherent polarimetric SAR (PolSAR) measurements over glaciers and ice sheets. Individual scattering components related to ice lenses and pipes are considered to model the subsurface scattering structure of ice sheets. The model is able to interpret the scattering amplitudes, their ratios at the different polarizations as well as the observed polarimetric phase differences. The co-polarization (HH-VV) phase difference is related to the structural anisotropy of the firn layer and can be used to estimate its thickness. The model is validated against L-band PolSAR data acquired by the E-SAR sensor of the German Aerospace Center (DLR) over the Austfonna ice cap in Svalbard during the ICESAR2007 campaign and available GPR profiles. L-band GPR profiles measured in spring 2007 by the Norwegian Polar Institute and the University of Oslo are also used to support the data analysis and interpretation

A Polarimetric First-Order Model of Soil Moisture Effects on the DInSAR Coherence

Changes in soil moisture between two radar acquisitions can impact the observed coherence in differential interferometry: both coherence magnitude | | and phase � are affected. The influence on the latter potentially biases the estimation of deformations. These effects have been found to be variable in magnitude and sign, as well as dependent on polarization, as opposed to predictions by existing models. Such diversity can be explained when the soil is modelled as a half-space with spatially varying dielectric properties and a rough interface. The first-order perturbative solution achieves–upon calibration with airborne L band data–median correlations � at HH polarization of 0.77 for the phase �, of 0.50 for | |, and for the phase triplets � of 0.56. The predictions are sensitive to the choice of dielectric mixing model, in particular the absorptive properties; the differences between the mixing models are found to be partially compensatable by varying the relative importance of surface and volume scattering. However, for half of the agricultural fields the Hallikainen mixing model cannot reproduce the observed sensitivities of the phase to soil moisture. In addition, the first-order expansion does not predict any impact on the HV coherence, which is however empirically found to display similar sensitivities to soil moisture as the co-pol channels HH and VV. These results indicate that the first-order solution, while not able to reproduce all observed phenomena, can capture some of the more salient patterns of the effect of soil moisture changes on the HH and VV DInSAR signals. Hence it may prove useful in separating the deformations from the moisture signals, thus yielding improved displacement estimates or new ways for inferring soil moisture

Estimation of snow and firn properties by means of multi-angular polarimetric SAR measurements

The retrieval of snow and firn properties on large scales is essential for a wide range of cryosphere applications and research questions, implying the necessity to employ remote sensing. Among the existing remote sensing techniques, synthetic aperture radars (SARs) allow monitoring polar regions independently of sun illumination and in (nearly) all-weather conditions. The penetration capability of microwave into dry snow, firn and ice makes SAR measurements sensitive to the internal structure of snow and ice layers. In this study, a physical model is explored to assess the potential to retrieve snow and firn properties, such as layer depth, density and anisotropy, from multi-angular polarimetric SAR measurements. The experimental validation is carried over the Austfonna ice cap, in Svalbard, using ALOS-2 PALSAR-2 data

Assessment of Paddy Rice Height: Sequential Inversion of Coherent and Incoherent Model

This paper investigates the evolution of canopy height of rice fields for a complete growth cycle. For this purpose, copolar interferometric Synthetic Aperture Radar (Pol-InSAR) time series data were acquired during the large across-track baseline (>1 km) science phase of the TanDEM-X mission. The height of rice canopies is estimated by three different model-based approaches. The first approach evaluates the inversion of the Random Volume over Ground (RVoG) model. The second approach evaluates the inversion of a metamodel-driven electromagnetic backscattering model by including a priori morphological information. The third approach combines the previous two processes. The validation analysis was carried out using the Pol-InSAR and ground measurement data acquired between May and September in 2015 over rice fields located in Ipsala district of Edirne, Turkey. The results of presented height estimation algorithms demonstrated the advantage of Pol-InSAR data. The combined RvoG model and EM metamodel height estimation approach provided rice canopy heights with errors less than 20 cm for the complete growth cycle