Polarimetric Synthetic Aperture Radar Data Research Articles

Forest Height Retrieval Based on the Dual PolInSAR Images

A new algorithm for forest height estimation based on dual polarimetric interferometric SAR data is presented in this study. The main objective is to consider the efficiency of the dual-polarization data compared to the full polarimetric images with respect to forest height retrieval. Accordingly, the forest height estimation based on the random volume over the ground model is examined using a geometrical procedure named the three-stage method. An exhaustive search polarization optimization technique is also applied to improve the results by employing the efficiency of all the polarization bases based on the four-dimensional lexicographic PolInSAR vector. The repeat-pass experimental SAR (ESAR) images, which include both L- and P-band full polarimetric data, are employed for the accuracy assessment of the dual PolInSAR data and the newly proposed method for forest height estimation. The experimental results on the L-band PolInSAR data show the ability of the dual PolInSAR data for forest height estimation with an average root mean square error (RMSE) of 4.97 m against Lidar data based on the conventional three-stage method. Additionally, the proposed method results in an accuracy of 2.95 m for forest height estimation, indicating its high potential for tree height retrieval.

Orthogonal Scattering Model-Based Three-Component Decomposition of Polarimetric SAR Data

New scattering models are constantly emerging to extract the detailed polarization information of ground targets. They contribute to the refined interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, an increasingly prominent problem lies ahead of model decomposition methods when there are similar scattering models for a decomposition scheme. It is difficult to separate the polarization power of similar scattering mechanisms reasonably and robustly. Therefore, in this paper, we first analyze the necessity of orthogonality between scattering models. Following this, we propose two mutually orthogonal rank-1 scattering models, which can degenerate to mostly current scattering models. The orthogonality and adaptability of scattering models are considered in the model derivation. Simulated PolSAR data, real ALOS-2/PALSAR-2, GF-3, and E-SAR data are selected to validate the proposed method. As shown by the results, first, the proposed method enhances the double-bounce scattering contribution in urban areas and maintains the volume-scattering contribution in vegetation areas because it separates the polarization power of different scattering mechanisms more reasonably. Second, the proposed method is powerful in the robust interpretation of different ground targets, resulting from the orthogonality of scattering models. These two characteristics of orthogonal scattering models are expected to play a positive role in large-scale applications, especially in land-cover classification.

A Polarization Stacking Method for Optimizing Time-Series Interferometric Phases of Distributed Scatterers

For time-series interferometric phases optimization of distributed scatterers (DSs), the SqueeSAR technology used the phase linking (PL) to extract the equivalent single-master (ESM) interferometric phases from the multilooking time-series coherence matrix. The Cramer–Rao lower bound (CRLB) for the PL describes the highest achievable estimation accuracy of the ESM phases, which depends on the number of looks and the time-series coherence magnitude matrix. With the abundance of time-series polarimetric SAR data, many scholars have studied the coherence magnitude-based polarimetric optimization methods for optimizing the DS’s time-series interferometric phases, for example, the widely-used exhaustive search polarimetric optimization (ESPO) algorithm. However, the traditional polarimetric optimization methods select the boundary extremums of the coherence region (CR) as the optimized complex coherence, which is usually biased from the free-noise one. Currently, in the polarimetric InSAR (PolInSAR) technology, Shen et al. innovatively considered polarimetric information as statistical samples and proposed the total power (TP) coherency matrix construction method for increasing the number of looks and reducing the interferometric phase noise. Therefore, to optimize the time-series interferometric phases for DS, this paper proposes performing a polarization stacking and extending the PolInSAR TP construction to the time-series PolInSAR (TSPolInSAR) data configuration, called the time-series TP (TSTP) method. Simulated and real experiments prove that the new TSTP construction method has better performance and higher efficiency than the single polarimetric and the traditional ESPO algorithms.

Offshore Bridge Detection in Polarimetric SAR Images Based on Water Network Construction Using Markov Tree

It is difficult to detect bridges in synthetic aperture radar (SAR) images due to the inherent speckle noise of SAR images, the interference generated by strong coastal scatterers, and the diversity of bridge and coastal terrain morphologies. In this paper, we present a two-step bridge detection method for polarimetric SAR imagery, in which the probability graph model of a Markov tree is used to build the water network, and bridges are detected by traversing the graph of the water network to determine all adjacent water branch pairs. In the step of the water network construction, candidate water branches are first extracted by using a region-based level set segmentation method. The water network is then built globally as a tree by connecting the extracted water branches based on the probabilistic graph model of a Markov tree, in which a node denotes a single branch and an edge denotes the connection of two adjacent branches. In the step of the bridge detection, all adjacent water branch pairs related to bridges are searched by traversing the constructed tree. Each bridge is finally detected by merging the two contours of the corresponding branch pair. Three polarimetric SAR data acquired by RADARSAT-2 covering Singapore and Lingshui, China, and by TerraSAR-X covering Singapore, are used for testing. The experimental results show that the detection rate, the false alarm rate, and the intersection over union (IoU) between the recognized bridge body and the ground truth are all improved by using the proposed method, compared to the method that constructs a water network based on water branches merging by contour distance.

Development of a generalized model to classify various land covers for ALOS-2 L-Band images using semantic segmentation

For a long time, Polarimetric Synthetic Aperture Radar (PolSAR) data was not available free of cost, so the applications were limited. With the recent increase in availability of PolSAR data due to missions like ALOS-2, UAVSAR and Sentinel, the data can be acquired through these sensors periodically. Since the volume of data is large, applying traditional classifiers and conventional machine learning algorithms becomes confounding, as in that case, manual feature extraction must be done by the researcher for training the model. To automate this feature extraction step and accelerate the process, many researchers have used various deep learning algorithms. However, most studies fail to tap into the potential of PolSAR data by not utilizing the complete range of complex data of float-32 bit-depth. The work in this paper suggests the development of a generalized deep learning model for nine elements of coherency matrix [T3] in the float-32 data-space from ALOS/PALSAR-2 L-Band sensor. Semantic segmentation is used to classify land-covers into various classes like water, settlement, forest, open land, wetlands etc. This is done through modification on the UNet backbone. The deep learning model is trained using data on San Francisco and New Delhi regions and tested on data from Mumbai region. It is observed that the classification accuracy for Mumbai region is “93.82%”. This kind of system can assist the decision makers like urban planners to take informed decisions.

Development of a novel approach for snow wetness estimation using hybrid polarimetric RISAT-1 SAR datasets in North-Western Himalayan region

Snow wetness estimates are critical inputs in the understanding of snow hydrological processes. With early warming in winters in the Himalayas, snowpack shows early signs of melting in February with increased snow wetness. Typically, in February, the snowpack comprises a mix of dry and wet snow layers. In the literature, methods for quantitative analysis of snow wetness are mostly based on fully polarimetric synthetic aperture radar (SAR) data. Methods based on the hybrid polarimetric SAR (PolSAR) data for snow monitoring are virtually inexistent. This study proposes a novel methodology for the estimation of snow wetness utilizing the C-band hybrid polarimetric RISAT-1 SAR dataset. Using radar remote sensing to analyze the behavior of such a snowpack requires information on the surface and volume scattering characteristics. The modeled generalized surface and volume scattering parameters (α), and (γ), based on the X-Bragg’s reflection coefficients and Fresnel transmission coefficients, are used for the inversion of surface and volume snow permittivities, respectively. The investigations are carried out for February 2014 for a study area in the Manali region in Himachal Pradesh, India. The retrieved snow estimates showed a coefficient of determination of 0.86 and a root mean square error of 0.667 with respect to in-situ measurements. Further, it was observed that the snow wetness estimates derived from the proposed method using RISAT-1 dataset outperformed the estimates based on fully polarimetric RADARSAT-2 dataset using the conventional Shi and Dozier method.

JPPL: A joint-polarization phase linking algorithm for phase optimization of TSPolInSAR data

• As a new polarimetric optimization, the joint-polarization phase linking (JPPL) is proposed to improve the phase quality. • The used joint diagonalization can extract the ESM phases from multi-polarimetric time-series coherence matrices. • Simulated experiment analyzes the interferometric phase quality of the HH, ESPO, and JPPL algorithms quantitatively. • Real experiment demonstrates that the proposed method reduces the speckle noise and restores the interferometric phase. In the time-series interferometric SAR (TSInSAR) technology, the phase linking (PL) algorithm can exploit all interferometric combinations to reconstruct the equivalent single master (ESM) interferograms for the phase quality improvement. According to the Cramer-Rao lower bound (CRLB) for PL, besides the number of spatial samples, the time-series coherence magnitude matrix determines the reconstruction performance of the ESM phases. With the abundance of time-series polarimetric SAR data, many polarimetric optimization algorithms for distributed scatterer (DS) have been introduced, mainly the averaged coherence magnitude maximization-based exhaustive search polarimetric optimization (ESPO) algorithm. However, traditional polarimetric optimization algorithms cannot work satisfactorily because of the unstable statistical characteristics. Similarly, the multi-temporal observations of each polarimetric channel contain interferometric information. From the perspective of redundant observation, introducing full-polarization information can increase the number of observations significantly. Therefore, based on the PL theory and the polarimetric optimization, this paper proposes to perform the joint diagonalization method to simultaneously extract the ESM interferometric phases of interest from three time-series coherence matrices in the Pauli basis polarizations, called the joint-polarization phase linking (JPPL) algorithm. In the experiments, the proposed JPPL algorithm can better improve the optimization performance of time-series interferometric phases of DSs than the HH and traditional ESPO algorithms in terms of speckle noise reduction and interferometric phase restoration.

Soil Moisture Retrieval Using SAR Backscattering Ratio Method during the Crop Growing Season

Soil moisture content (SMC) is an indispensable basic element for crop growth and development in agricultural production. Obtaining accurate information on SMC in real time over large agricultural areas has important guiding significance for crop yield estimation and production management. In this study, the paper reports on the retrieval of SMC from RADARSAT-2 polarimetric SAR data. The proposed SMC retrieval algorithm includes vegetation correction based on a ratio method and roughness correction based on the optimal roughness method. Three vegetation description parameters (i.e., RVI, LAI, and NDVI) serve as vegetation descriptors in the parameterization of the algorithm. To testify the vegetation correction result of the algorithm, the water cloud model (WCM) was compared with the algorithm. The calibrated integrated equation model (CIEM) was employed to describe the backscattering from the underlying soil. To improve the accuracy of SMC retrieval, the CIEM model was optimized by using the optimal roughness parameter and the normalization method of reference incidence angle. Validation against ground measurements showed a high correlation between the measured and estimated SMC when the NDVI serves as vegetation descriptor (R2 = 0.68, RMSE = 4.15 vol.%, p < 0.01). The overall estimation performance of the proposed SMC retrieval algorithm is better than that of the WCM. It demonstrates that the proposed algorithm has an operational potential to estimate SMC over wheat fields during the growing season.

A Novel Topography Retrieval Algorithm Based on Single-Pass Polarimetric SAR Data and Terrain Dependent Error Analysis

Polarimetric synthetic aperture radar (PolSAR) data provide an alternative way for topography retrieval, especially when limited PolSAR data are available. This article proposes a novel topography retrieval algorithm based on the Lambertian backscatter model that further improves the vertical precision of digital elevation model (DEM) generation and requires only one flight. The key idea of the proposed algorithm is to avoid data fluctuations caused by the ratio of the azimuth slope angle to the polarimetric orientation angle (POA). The previous research has confirmed the feasibility of generating a DEM based on single-pass PolSAR data, but its effect on the quality of reference DEM has not been well-explained. To analyze this effect, a large number of experiments on DEM with different resolutions are conducted. In addition, an in-depth analysis of non-linear and terrain-dependent errors is performed. The L-band PolSAR data of NASA/JPL TOPSAR and ALOS-2 PALSAR-2 and interferometric SAR (InSAR) DEM data are used to verify the proposed algorithm. The experimental results show that PolSAR data can be used as an additional reliable information source for DEM fusion under certain conditions to improve the quality of public DEM.

PolSAR Scene Classification via Low-Rank Constrained Multimodal Tensor Representation

Polarimetric synthetic aperture radar (PolSAR) data can be acquired at all times and are not impacted by weather conditions. They can efficiently capture geometrical and geographical structures on the ground. However, due to the complexity of the data and the difficulty of data availability, PolSAR image scene classification remains a challenging task. To this end, in this paper, a low-rank constrained multimodal tensor representation method (LR-MTR) is proposed to integrate PolSAR data in multimodal representations. To preserve the multimodal polarimetric information simultaneously, the target decompositions in a scene from multiple spaces (e.g., Freeman, H/A/α, Pauli, etc.) are exploited to provide multiple pseudo-color images. Furthermore, a representation tensor is constructed via the representation matrices and constrained by the low-rank norm to keep the cross-information from multiple spaces. A projection matrix is also calculated by minimizing the differences between the whole cascaded data set and the features in the corresponding space. It also reduces the redundancy of those multiple spaces and solves the out-of-sample problem in the large-scale data set. To support the experiments, two new PolSAR image data sets are built via ALOS-2 full polarization data, covering the areas of Shanghai, China, and Tokyo, Japan. Compared with state-of-the-art (SOTA) dimension reduction algorithms, the proposed method achieves the best quantitative performance and demonstrates superiority in fusing multimodal PolSAR features for image scene classification.

Evaluating irrigation status in the Mekong Delta through polarimetric L-band SAR data assimilation

For sustainable food production in the Mekong Delta, local information on irrigation status is essential for allocating water resources efficiently at the community level. ALOS-2 PALSAR-2 L-band SAR can be used to detect submerged and nonsubmerged soil covered by rice plants at a spatial resolution compatible with field observation but a low temporal resolution. In this study, a new multiscale data assimilation technique is developed to estimate the spatiotemporal dynamics of field water levels at a temporal resolution appropriate to inform decision-making on when to initiate irrigation. The method employs an irrigation model based on parameters representing farmers' irrigation practices. ALOS-2 PALSAR-2 data are used to derive the hydrological parameters of the model, including an irrigation parameter representing how deep the field water level dropped until the next round of irrigation was initiated. We developed observation operators for the soil submersion status under vegetation and spatial submersion percentages instead of assimilating soil moisture products. The study uses ALOS-2 PALSAR-2 data (25–100 m spatial resolution, approximately every 42 days) over the Mekong Delta and experimental data collected in situ for model initialization and validation. The estimated irrigation parameter controlling the maximum depth of field water level showed spatio-temporal consistency with the ground-observed value (RMSE = 4.24 cm). The values of the parameters also showed spatial consistency with respect to DEM data; he paddies with low irrigation model parameter values were prone to be located in low-elevation zones (<2 m), whereas high model parameters values tended to be located in paddies in the high-elevation zones (>3 m).The results show promising applications using L-band SAR observations for monitoring paddy field water level, for irrigation practices and for estimation of the water consumption and of methane emissions.

PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector

The detection of ships on the open sea is an important issue for both military and civilian fields. As an active microwave imaging sensor, synthetic aperture radar (SAR) is a useful device in marine supervision. To extract small and weak ships precisely in the marine areas, polarimetric synthetic aperture radar (PolSAR) data have been used more and more widely. We propose a new PolSAR ship detection method which is based on a keypoint detector, referred to as a PolSAR-SIFT keypoint detector, and a patch variation indicator in this paper. The PolSAR-SIFT keypoint detector proposed in this paper is inspired by the SAR-SIFT keypoint detector. We improve the gradient definition in the SAR-SIFT keypoint detector to adapt to the properties of PolSAR data by defining a new gradient based on the distance measurement of polarimetric covariance matrices. We present the application of PolSAR-SIFT keypoint detector to the detection of ship targets in PolSAR data by combining the PolSAR-SIFT keypoint detector with the patch variation indicator we proposed before. The keypoints extracted by the PolSAR-SIFT keypoint detector are usually located in regions with corner structures, which are likely to be ship regions. Then, the patch variation indicator is used to characterize the context information of the extracted keypoints, and the keypoints located on the sea area are filtered out by setting a constant false alarm rate threshold for the patch variation indicator. Finally, a patch centered on each filtered keypoint is selected. Then, the detection statistics in the patch are calculated. The detection statistics are binarized according to the local threshold set by the detection statistic value of the keypoint to complete the ship detection. Experiments on three data sets obtained from the RADARSAT-2 and AIRSAR quad-polarization data demonstrate that the proposed detector is effective for ship detection.

Classification of GaoFen-3 full-polarization data based on combining polarization characteristics

To explore potential applications of GaoFen-3 (GF-3) fully polarimetric synthetic aperture radar (SAR) data in ground-feature classification, we selected Jiangxiang Town, Nanchang County as the research area and used GF-3 quad-polarization strip I (QPSI) data for classification. Pauli decomposition, Krogager decomposition, and the H / A / α decomposition model were used to decompose the GF-3 full-polarization data; combined with the backscattering coefficient values of GF-3’s HH, HV, VH, and VV polarization data, feature optimization was performed, and the optimal ground-object features for feature classification were obtained. The random forest (RF) method was used to classify the optimal features, and the results were then compared and verified. The results demonstrate that the component after polarization decomposition of H / A / α is more important in classification than the original backscattering coefficient values of HH, HV, VH, and VV. GF-3 single-phase SAR data were used for ground-feature classification in a crop-intensive area of South China, and the optimal feature optimization results included H / A / α decomposition and synthesis components of the alpha, anisotropy, red, entropy, blue, green, and polarization backscattering coefficients of VV. The RF method’s classification accuracy was higher than that of the maximum-likelihood, support-vector-machine, and neural-network methods. Using this approach, GF-3 data classification’s overall accuracy was up to 78.17%, and the Kappa coefficient was 0.738. This study has clear practical significance and reference value for the selection of classification methods for ground features based on GF- 3 data in cloudy and rainy areas.

TCSPANet: Two-Staged Contrastive Learning and Sub-Patch Attention Based Network for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) image classification has achieved great progress, but there still exist some obstacles. On the one hand, a large amount of PolSAR data is captured. Nevertheless, most of them are not labeled with land cover categories, which cannot be fully utilized. On the other hand, annotating PolSAR images relies more on domain knowledge and manpower, which makes pixel-level annotation harder. To alleviate the above problems, by integrating contrastive learning and transformer, we propose a novel patch-level PolSAR image classification, i.e., two-staged contrastive learning and sub-patch attention based network (TCSPANet). Firstly, the two-staged contrastive learning based network (TCNet) is designed for learning the representation information of PolSAR images without supervision, and obtaining the discrimination and comparability for actual land covers. Then, resorting to transformer, we construct the sub-patch attention encoder (SPAE) for modelling the context within patch samples. For training the TCSPANet, two patch-level datasets are built up based on unsupervised and semi-supervised methods. When predicting, the classification algorithm, classifying or splitting, is put forward to realise non-overlapping and coarse-to-fine patch-level classification. The classification results of multi-PolSAR images with one trained model suggests that our proposed model is superior to the compared methods.

A Hybrid Polarimetric Target Decomposition Algorithm with Adaptive Volume Scattering Model

Previous studies have shown that scattering mechanism ambiguity and negative power issues still exist in model-based polarization target decomposition algorithms, even though deorientation processing is implemented. One possible reason for this is that the dynamic range of the model itself is limited and cannot fully satisfy the mixed scenario. To address these problems, we propose a hybrid polarimetric target decomposition algorithm (GRH) with a generalized volume scattering model (GVSM) and a random particle cloud volume scattering model (RPCM). The adaptive volume scattering model used in GRH incorporates GVSM and RPCM to model the volume scattering model of the regions dominated by double-bounce scattering and the surface scattering, respectively, to expand the dynamic range of the model. In addition, GRH selects the volume scattering component between GVSM and RPCM adaptively according to the target dominant scattering mechanism of fully polarimetric synthetic aperture radar (PolSAR) data. The effectiveness of the proposed method was demonstrated using AirSAR dataset over San Francisco. Comparison studies were carried out to test the performance of GRH over several target decomposition algorithms. Experimental results show that the GRH outperforms the algorithms we tested in this study in decomposition accuracy and reduces the number of pixels with negative powers, demonstrating that the GRH can significantly avoid mechanism ambiguity and negative power issues.

Monitoring the Wetland of the Yellow River Delta by Combining GF-3 Polarimetric Synthetic Aperture Radar and Sentinel-2A Multispectral Data

Wetlands in estuary deltas functionally protect biodiversity, store water, and regulate ecological balance. However, wetland monitoring accuracy is low when using only synthetic aperture radar (SAR) images or optical images. This study proposes a novel method for extracting ground objects in a wetland using principal component analysis (PCA) and random forest (RF) classification, which combines the features of fully polarimetric SAR images and optical images. Firstly, polarization decomposition features and texture features were extracted based on polarimetric SAR data, and spectral features were extracted based on optical data. Secondly, the optical image was registered to SAR image. Then PCA was performed on the nine polarimetric features of the SAR images and the four spectral features of the optical images to obtain the first two principal components of each. After combining these components, a RF classification algorithm was used to extract the objects. The objects in the Yellow River Delta wetland were successfully extracted using our proposed method with Gaofen-3 fully polarimetric SAR data and Sentinel-2A optical data acquired in November 2018. The overall accuracy of the proposed method was 86.18%, and the Kappa coefficient was 0.84. This was an improvement of 18.96% and 0.22, respectively, over the GF-3 polarimetric features classification, and 11.02% and 0.13, respectively, over the Sentinel-2A spectral features classification. Compared with the results of the support vector machine, maximum likelihood, and minimum distance classification algorithms, the overall accuracy of the RF classification based on joint features was 2.03, 5.69, and 23.36% higher, respectively, and the Kappa coefficient was 0.03, 0.07, and 0.27 higher, respectively. Therefore, this novel method can increase the accuracy of the extraction of objects in a wetland, providing a reliable technical means for wetland monitoring.

Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence

Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but they need a high volume of labeled samples, which are not usually available in practice, or they cause a high computational burden for implementation. In this work, instead of spending cost for network training, the inherent nature of PolSAR image is used for generation of convolutional kernels for extraction of deep and robust features. Moreover, extraction of diverse scattering characteristics contained in the coherency matrix of PolSAR and fusion of their output classification results with a high confidence have high impact in providing a reliable classification map. The introduced method called discriminative features based high confidence classification (DFC) utilizes several approaches to deal with difficulties of PolSAR image classification. It uses a multi-view analysis to generate diverse classification maps with different information. It extracts deep polarimetric-spatial features, consistent and robust with respect to the original PolSAR data, by applying several pre-determined convolutional filters selected from the important regions of image. Convolutional kernels are fixed without requirement to be learned. The important regions are determined with selecting the key points of image. In addition, a two-step discriminant analysis method is proposed to reduce dimensionality and result in a feature space with minimum overlapping and maximum class separability. Eventually, a high confidence decision fusion is implemented to find the final classification map. Impact of multi-view analysis, selection of important regions as fixed convolutional kernels, two-step discriminant analysis and high confidence decision fusion are individually assessed on three real PolSAR images in different sizes of training sets. For example, the proposed method achieves 96.40% and 98.72% overall classification accuracy by using 10 and 100 training samples per class, respectively in L-band Flevoland image acquired by AIRSAR. Generally, the experiments show high efficiency of DFC compared to several state-of-the-art methods especially for small sample size situations.

Offshore Oil Platform Detection in Polarimetric SAR Images Using Level Set Segmentation of Limited Initial Region and Convolutional Neural Network

Offshore oil platforms are difficult to detect due to the complex sea state, the sparseness of target distribution, and the similarity of targets with ships. In this paper, we propose an oil platform detection method in polarimetric synthetic aperture radar (PolSAR) images using level set segmentation of a limited initial region and a convolutional neural network (CNN). Firstly, to reduce the interference of sea clutter, the offshore strong scattering targets were initially detected by the generalized optimization of polarimetric contrast enhancement (GOPCE) detector. Secondly, to accurately locate the contour of targets and eliminate false alarms, the coarse results were refined using an improved level set segmentation method. An algorithm for splitting and merging the smallest enclosing circle (SMSEC) was proposed to cover the coarse results and obtain the initial level set function. Finally, the LeNet-5 CNN model was used to classify the oil platforms and ships. Experimental results using multiple sets of polarimetric SAR data acquired by RADARSAT-2 show that the performance of the proposed method, including the detection rate, the false alarm rate, and the Intersection over Union (IOU) index between the extracted ROI and the ground truth, is better than the performance of a method that combines a GOPCE detector and a support vector machine classifier.

Deforestation detection using scattering power decomposition and optimal averaging of volume scattering power in tropical rainforest regions

Synthetic aperture radar (SAR) has been a powerful tool for deforestation detection in tropical rainforests. Polarimetric SAR (POLSAR) data are acquired in a quad-polarization mode, and L-band POLSAR data in particular are one of the few SAR data types that preserve the dielectric properties and structures of the scatterer. POLSAR data consist of 2 × 2 scattering matrices and consequently offer superior target recognition compared with dual-polarization data. In this study, we applied scattering power decomposition suitable for detecting deforestation in near real-time to POLSAR data obtained from the Earth observation sensor Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2). The reflection symmetry condition is known to apply to natural distributed objects (i.e., the cross-correlation between co- and cross-polarization data is zero). Inspired by this, we theoretically and experimentally examined the volume scattering power component to distinguish natural forests from the surrounding area. Two important results were verified for natural forests: as the window size of the ensemble average increases, (i) the coherency matrix approaches a simple theoretical form and (ii) the volume scattering power becomes dominant among the scattering power components. Based on these results, we constructed an algorithm that applies scattering power decomposition for detecting deforestation. We produced reference data using high-resolution optical images and evaluated the performance of the derived deforestation map in the Amazon natural forest when employing various window sizes for the ensemble average. At an optimal window size of 15 × 15 pixels, the deforestation detection performance reached a user's accuracy of 94.9% ± 1.5%, a producer's accuracy of 72.3% ± 1.2%, and a kappa coefficient of 0.816 ± 0.0039. Sparse trees left after logging increased the volume scattering power and reduced the producer's accuracy. The proposed algorithm can contribute to deforestation detection with slightly lower accuracy than that of the annual map provided by Global Forest Change. Further, the proposed algorithm is robust to the seasonal variations in tropical rainforests and temporal variations in the deforestation process. Consequently, the proposed algorithm employing the six-component scattering power decomposition method can be utilized in near real-time without considering applicable areas in tropical rainforests. Subsequently, we applied our algorithm to dual-polarization data, which are acquired by PALSAR-2 much more frequently than POLSAR data. The false detection rate did not increase when using the dual-polarization data; however, the omission error increased considerably compared with that when using POLSAR data owing to the low total power obtained from the dual-polarization data.

Polarimetric Calibration and Spatio‐Temporal Polarimetric Distortion Analysis of UAVSAR PolSAR Data

AbstractThe polarimetric distortions present in the polarimetric SAR (PolSAR) data sets leads to unexpected results from the polarimetric decomposition and classification models, which in turn result in the ground target misinterpretation. The most important polarimetric distortions in airborne PolSAR data sets are the channel imbalance and crosstalk. The minimization of these polarimetric distortions using polarimetric calibration techniques is very important to carry out the quantitative analysis using PolSAR data and time series analysis and to compare results between different sensors. In the current state of the art, the spatial variability of the polarimetric distortions at different slant‐range positions in the data set was not completely analyzed and also the temporal variability of the polarimetric distortions using multiple data sets needs to be more investigated. Therefore, the main objectives of this study were to develop an operational processor to perform the external polarimetric calibration of the L‐band Uninhibited Aerial Vehicle Synthetic Aperture Radar system and to analyze the spatio‐temporal behavior of the polarimetric distortions. The polarimetric distortion matrix (PDM) was formulated after considering the crosstalk and channel imbalance polarimetric distortions to derive the scattering matrix free from all kinds of polarimetric distortions from the observed scattering matrix. The Quegan, improved Quegan, and Ainsworth methods for polarimetric calibration were implemented to solve the PDM. The residual channel imbalance and crosstalk after polarimetric calibration were estimated to compare the calibration efficiency of these methods. After that, a spatio‐temporal analysis was carried out with several data sets to assess the spatio‐temporal behavior of these polarimetric distortions. The polarimetric signatures and the coherency matrix behavior of the trihedral corner reflectors were used to analyze the ground target characterization improvement after polarimetric calibration. The results obtained from this study revealed that the crosstalk is the major cause for the polarimetric distortions and the channel imbalance and phase bias present in the data sets were not high. From the spatio‐temporal analysis, it was found that the variability of these polarimetric distortions both in spatial and temporal domains was minimum and can be discarded without causing any considerable error in the polarimetric calibration process.