Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing approaches, whether end-to-end semantic parsing or stepwise agent-based reasoning—often suffer from structural inaccuracies and prohibitive computational costs, particularly when processing intricate queries over large knowledge graphs. To address these limitations, we introduce SEAL, a novel two-stage semantic parsing framework grounded in self-evolving agentic learning.In the first stage, a large language model (LLM) extracts a minimal S-expression core that captures the essential semantics of the input query. This core is then refined by an agentic calibration module, which corrects syntactic inconsistencies and aligns entities and relations precisely with the underlying knowledge graph. The second stage employs template-based completion, guided by question-type prediction and placeholder instantiation, to construct a fully executable S-expression. This decomposition not only simplifies logical form generation but also significantly enhances structural fidelity and linking efficiency.Crucially, SEAL incorporates a self-evolving mechanism that integrates local and global memory with a reflection module, enabling continuous adaptation from dialog history and execution feedback without explicit retraining. Extensive experiments on the SPICE benchmark demonstrate that SEAL achieves state-of-the-art performance, especially in multi-hop reasoning, comparison, and aggregation tasks. The results validate notable gains in both structural accuracy and computational efficiency, underscoring the framework's capacity for robust and scalable conversational reasoning.

Tomography synthetic aperture radar (TomoSAR) is a cutting-edge radar observation technique that has the ability to produce three-dimensional images and can effectively extract forest vertical structure parameters, including forest height, a key forest parameter closely related to forest biomass and carbon storage. However, the phase errors in the TomoSAR data are unavoidable due to the elements such as orbit errors, which can seriously affect the quality of tomographic imaging and thus affect the accuracy of forest parameter extraction. To address this issue, various methods have been proposed. Nevertheless, they still exhibit restrictions when addressing phase errors with complex trends. To solve such problem, a novel method was developed and implemented in this paper, which includes two steps and remove parts of the phase errors with different trends sequentially. First, a wavelet decomposition and polynomial fitting-based approach was applied to each track to remove the slowly but significantly spatially-varying part of the phase errors. Secondly, the modified autofocusing algorithm is proposed to correct the remaining phase errors, which adopted the two-dimensional image entropy as the optimization indicator, providing stronger robustness compared to traditional indicator. Furthermore, in order to overcome the initial value dependency of the traditional search method, the proposed autofocusing algorithm used the particle swarm algorithm as search engine. After the phase error correction, the forest height was extracted by identifying the upper and lower boundary of the forest from the corrected TomosAR profiles. Two P-band datasets obtained in north China are adopted to examine the proposed phase error correction method. Experimental results show that compared with traditional autofocusing algorithm, the proposed method can achieve higher quality tomographic imaging results. On the basis of TomoSAR imaging, higher precision forest height extraction is obtained based on the new method.

Crop field edge is the key characteristic of agricultural crop management. Edge detection with dual-polarization SAR time series have been widely studied, with the data advantages of sensitivity to crop growth. Existing methods rarely consider time-varying dynamic polarimetric characteristics, making it difficult to detect complete crop field edges. Based on this, this proposes a joint edge strength, which combines two kinds of polarimetric distances with a novel spatial-temporal homogeneity measure. This measure applies spatial- and temporal-varying contexts to pre-identify edge and homogenous area, and adaptatively allocate various distances in one pixel. There are 14 Sentinel-1 SAR time series images are utilized to evaluate the proposed method. By the comparison of the visual differences, our method has lower missing rate and lower false alarm rate than conventional methods.

Multibaseline interferometric synthetic aperture radar (InSAR), multibaseline polarimetric InSAR (PolInSAR), and SAR tomography (TomoSAR) are the advanced techniques for forest height inversion. However, accurate large-scale inversion using these techniques still faces two key problems: 1) for InSAR and PolInSAR, the existing inversion models fail to accurately describe the vertical structure, reducing the inversion performance; 2) for TomoSAR, its baseline configuration requirement is too high to reconstruct the vertical structure of the large-scale forested area. To solve these two problems, this paper proposes a high inversion accuracy forest vertical structure heterogeneity (FVSH) coherent scattering model and a small-scale tomography-aided multibaseline (Pol)InSAR (STMI) forest height inversion framework, which provide a synergic observation and inversion scheme of these multibaseline SAR techniques using machine learning approaches. The different-frequency InSAR and PolInSAR data acquired above the different types of forested areas are selected for validation. The experimental results show the effectiveness of the proposed framework.

China's first group of L-band interferometric synthetic aperture radar (InSAR) satellites LuTan-1 (LT-1) were successfully launched in 2022. This satellite mission is mainly designed for global digital elevation model (DEM) mapping and rapid surface deformation monitoring. However, the group of satellites also acquired L-band SAR observations in both bistatic and monostatic modes, which presented an opportunity for large-scale forests vertical structure detection and underlying terrain estimation. This article aims at vegetation height and underlying terrain inversion using LT-1 spaceborne bistatic InSAR data with a model-based method. Preliminary cross-validation was performed on the inversion results using terrain and forests height products obtained from spaceborne ICESat-2 products. Both results demonstrated the potential of LT-1 for large-scale vegetation height and underlying terrain inversion. This work will also provide reference for the upcoming TanDEM-L project in studying the regional and global forests parameter estimation using spaceborne bistatic InSAR data.

The intent of this paper is to make an attempt at a large-scale forest height estimation technique framework construction via multi-data fusion of Quad-Pol TanDEM-X and GEDI Data. Faced with complex forest scenes, such as diverse tree species, sparse forest distribution and complex terrain, the authors design a strategy by fusing the traditional Random Volume over Ground (RVoG) model with ancillary forest height information from GEDI observations using BP neural network trained by sparse GEDI samples. To guarantee the accuracy for canopy height retrievals, the vertical wavenumber, as the critical parameter for RVoG construction, are refined by GEDI samples. This a large-scale forest height estimation technique framework is implemented over sparse forest in Danling county of China using quad-Pol TanDEM-X and GEDI Data. The results show that the proposed technique framework can meet the requirement of a large-scale forest height mapping in a resolution of 10m, with an average accuracy of 3.80 m, which is better than that of the traditional three-stage inversion method based on RVoG model.

Iceberg information in polar regions is crucial for various applications. Synthetic aperture radar (SAR) satellites provide high-resolution remote sensing images without being affected by weather conditions, which are widely used for iceberg monitoring. Most current studies track icebergs by shape similarity and use the distance between the centroids of the icebergs as the offset between the two temporal images. However, it is difficult to characterize icebergs comprehensively with a single shape similarity, the matching performance of the traditional shape similarity-based iceberg tracking method depends on the profile extraction accuracy in the iceberg detection. Furthermore, the limited coverage of satellite remote sensing images prevents the full capture of all icebergs. The drift of these icebergs in the time-series image exhibits shape variations, leading to a mismatch in the centroids, affecting the accuracy of drift velocity calculations. To address these issues, this letter proposes an object-oriented rotated intensity matching (OORIM)-based iceberg drift method that considers both the boundary shape and the intensity similarity of iceberg objects and obtains both offset information and rotation angle simultaneously. We successfully tracked 21 icebergs from Sentinel-1 SAR images near the Weddell Sea, and the results demonstrate the superiority of this method compared with other methods in terms of the accuracy of iceberg tracking. Specifically, the correct tracking accuracies of the centroid distance histogram (CDH), angle distance vector (ADV), and proposed OORIM methods are 90.5%, 66.7%, and 100%, respectively.

Precision agriculture management relies on the delineation of crop field edges. Multi-polarization SAR technology has the ability to penetrate clouds and capture morphological structures or moistures, suited for extracting crop field edges. Due to the time-dependent characteristics and phenological evolutions of crops, the methods with single-date data are difficult to detect complete edges. Moreover, the existing methods fail to extract the dynamic time-varying patterns, limiting the improvement of edge detection accuracy. Based on this, this paper proposes a novel crop field edge detection method based on the time-varying polarimetric characteristics. First, a spatial-temporal homogeneity measure is proposed to pre-identify the edge and homogenous area, for guiding the adaptive calculation of edge strength. Based on the time-series polarimetric stationarity and the trace moment estimation theory, the proposed measure enlarges the separating degree of various crop parcels. Second, a joint edge strength is proposed to enlarge strength contrast between edge and homogenous area. With the spatial-temporal homogeneity measure, it combines the similarity with the root mean square and the similarity with time-series average covariance matrix. Based on the advantages of two kinds of similarities, it highlights the field edges and reduces the impact of speckle noises. Evaluated by 8 quad-polarization and 14 dual-polarization SAR images, the proposed edge detection method achieves better visual presentations and detection accuracies than traditional methods. With the statistics of the signal-noise ratio (SNR), the joint edge strength also has higher strength contrast than conventional strengths. The relevant codes can be found in https://github.com/DawnHanGeo/TSPolEdge.git.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has been widely used to obtain high-precision sub-canopy topography. However, due to the vegetation cover over densely forested areas, the ground photons are sparse, which makes it difficult to accurately estimate the sub-canopy topography over densely forested areas. In this paper, we proposed a novel method for retrieving sub-canopy topography over densely forested areas from ICESat-2/ATL03 data. First, the proposed method used an improved elevation frequency histogram statistics (imEFHS) method to obtain candidate ground seed photons (GSPs). In densely forested areas, the obtained candidate GSPs are easily misclassified as canopy photons. Therefore, we performed a gradient-constrained morphological operation to identify erroneous GSPs. Finally, an erroneous GSPs refinement approach was derived to correct erroneous GSPs over densely forested areas. In addition, the sub-canopy topography can be presented by the refined GSPs with cubic spline interpolation. ICESat-2/ATL03 data acquired over densely forested areas were selected for testing the proposed method. The results in the given test sites show that the proposed method can extract sub-canopy topography accurately, with a root-mean-square error (RMSE) of 1.71 m over densely forested areas. We also compared the retrieved sub-canopy topography results with NASA ATL08 terrain samples. We found that the ratio of useful sub-canopy topography results (Residual2) between the retrieved sub-canopy topography results and the reference high-precision DTMs reached 0.93, which is much higher than that of the ATL08 terrain samples (R2= 0.63).

The existing exhaustive search polarimetric optimization (ESPO) method exploiting polarimetric diversity has been widely used to improve the phase quality in polarimetric interferometric synthetic aperture radar (PolInSAR). However, the optimization ceiling of the ESPO method based on the coherence metric is largely restricted by the variation of scattering mechanisms in the spatial domain, especially for high-resolution SAR data. To this end, this letter proposes an improved ESPO (ImESPO) method that considers the changes of the scattering mechanisms in local windows and applies it to the time-series phase optimization framework. Both simulated and real experiments demonstrated the effectiveness of the proposed method, which shows that the changes of the scattering mechanisms are more serious in the large window or for high-resolution SAR data. In addition, the effects of the filtering window size and the number of interferograms on optimization were also analyzed in detail.

The uncertainty of crop phenological cycle is an important issue in crop classification with time series PolSAR data. The time series alignment algorithm represented by dynamic time warping (DTW) can supply a potential solution, which realigns curves based on shape matching, dealing with the distortion of feature curves caused by uncertain crop phenological development. However, previous studies mainly focused on shape characteristics of time-varying feature curves, which is hard to comprehensively evaluate the similarity degree of crop phenological cycles. Furthermore, it ignored the differences in scattering signal and polarimetric statistical distribution of crops, which limited the accuracy of crop classification. In this letter, a novel crop classification method based on phenology alignment is proposed. Firstly, the dual-branch time series alignment method is proposed, including the time-weighted dynamic time warping (TWDTW) alignment and the Wishart distance-based TWDTW (WD-TWDTW) alignment, which combines the feature curve characteristics and the polarimetric statistical information to correctly describe the similarity degree of phenological cycles. Secondly, a multi-similarity measure (including shape similarity, feature similarity and polarimetric similarity) is defined to improve capacity of crop discrimination. The multi-similarity measure can describe the differences of crop types from three aspects, including crop growth trend, growth status, and statistical distribution. The proposed method is evaluated with time series full-polarization Radarsat-2 data in Flevoland area. The results show that our method is superior to traditional method with single TWDTW alignment and shape similarity, and the corresponding overall accuracy is improved by 6%.

Photon cloud data filtering is crucial when obtaining forest vertical structure parameters from photon-counting LiDAR data. The proposed method, for the first time, takes into account the influence of the density difference between canopy photons and ground photons. A moving overlapping window approach is introduced to reduce the impact of an uneven background noise environment first. In each window, a modified elevation histogram statistics vector in the elevation direction is proposed to increase the density difference between signal and noise photons while also reducing the density difference between canopy and ground photons. The filtering results show that the average overall accuracy (OA) and standard deviation of the proposed method reach almost 0.99 and 0.01, respectively, which are much better results than those of the other existing filtering methods. Specifically, with the increase in the ratio of canopy photons to ground photons, the F-measure value of the proposed method reaches almost 0.99, and is also stable, which demonstrates that the proposed approach can almost completely eliminate the influence of the density difference between canopy photons and ground photons on the filtering results. In addition, the forest canopy heights obtained based on the proposed filtering method achieve the lowest root-mean-square error (RMSE) value of 3.18 m, compared to the other filtering methods. In summary, the proposed photon cloud data filtering method can retrieve reliable forest canopy height information from photon cloud data, and outperforms the other compared filtering methods in the given test site.

Device-free gesture recognition (DFGR) is an emerging technique which could leverage the influence of human gestures on surrounding wireless signals to recognize gestures. It has gained widespread attention due to its promising prospect of empowering pervasive wireless devices with the sensing ability. Due to the inconsistency of the feature distribution in different scenarios, a well-trained DFGR system often fails to get satisfactory performance in cross-scenario conditions. Researchers have done valuable exploration on alleviating the feature distribution shift from a global distribution point of view. However, global feature distribution alignment could not solve the feature distribution shift problem completely. In this article, we develop a self-adaptive adversarial learning network which could further reduce the feature distribution shift through aligning the local feature distribution. Specifically, we design an adversarial network which is consisted of a feature extractor, a scenario discriminator, and two diverse classifiers. It could evaluate the degree of local feature distribution alignment by analyzing the prediction inconsistent of the classifiers. We design a self-adaptive adversarial loss which can be adjusted adaptively according to the degree of local alignment. If the features have been aligned locally, we reduce their impact on the loss to protect these aligned features. Otherwise, we increase their influence to accelerate the training process. The extensive experiments conducted on a designed mmWave testbed demonstrate that the proposed method could achieve an accuracy of at least 4% higher than those of existing cross-scenario DFGR methods, while the number of training iterations can be reduced by nearly half.

In this letter, we propose a new scattering model to describe the polarimetric scattering information of the real part of$T_{23}$in the coherency matrix. To achieve this goal, by combining the dihedral corner reflector scattering model and the polarimetric orientation angle (POA), a rotated dihedral scattering model is proposed. The proposed model is embedded into Singh’s six-component decomposition model, and we further develop a seven-component decomposition model. The proposed method was validated by polarimetric synthetic aperture radar (SAR) data sets acquired by the ALOS-2/PALSAR-2 and AIRSAR systems. The results show that, compared with the existing decomposition methods, the proposed method has a superior ability to distinguish oriented buildings from vegetation.

As a supplement to synthetic aperture radar interferometry (InSAR), multiple-aperture InSAR (MAI) can measure along-track surface deformation, but it is limited by ionospheric path delays, especially with the L- or P-band data. In this letter, we propose a method to correct an ionospheric error in the MAI measurement based on the along-track gradient and 1-D linear fitting. The method depends on the uniqueness of the spatial variation of the along-track gradient of ionospheric error in MAI measurements, which can be well distinguished from other components, such as deformation by using 1-D linear fitting. The method is first evaluated by employing the L-band ALOS-2 PALSAR-2 dataset of the 2019 Ridgecrest earthquake, U.S., and then applied to estimate glacial movements of Grove Mountain, Antarctica, with the ALOS-2 PALSAR-2 dataset.

Isolating the orbital error from the interferometric synthetic aperture radar (InSAR) observations is a great challenge, especially in the presence of tectonic deformation due to their similar spatial patterns. The influence of orbital error is systematic, which can reduce the reliability of deformation monitoring. In this letter, we propose a method to isolate the orbital error from the multitemporal InSAR (MTInSAR) derived tectonic deformation based on the wavelet multiresolution analysis and independent component analysis (ICA). Starting from the sequential interferometric phase of unwrapping, the tectonic deformation and orbital error are firstly extracted from the interferometric phase by wavelet analysis based on their longwavelength spatial patterns, and ICA is then used to isolate the orbital error from the tectonic deformation according to the different temporal characteristics of the two types of signals. In the simulation experiment, the root-mean-square error (RMSE) of the isolated orbital error is 2.6 mm. Experiments with real data in Southern California show that the proposed method can successfully separate the orbital error from the tectonic deformation, and the InSAR deformation rates are in good agreement with the GPS observations.

Vegetation height estimation of desert ecosystems is important for understanding the groundwater cycle. ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) provides an opportunity to measure vegetation heights on a global scale. This letter proposed a method for retrieving low and sparse vegetation heights in desert ecosystems. Considering the significant difference in density between the vegetation photons and the ground photons, the ground photons were removed based on the terrain-adaptive method first. The localized density parameter was then introduced to distinguish the vegetation photons and the noise photons. Finally, the vegetation heights were obtained by the elevation percentile approach. The proposed method was tested using the ICESat-2 data acquired over a desert located in Arizona. The vegetation height results derived by the proposed method have an RMSE of 0.78 m which is significantly less than that of ATL08 with an RMSE of 4.26 m, which demonstrates it is feasible to extract low and sparse vegetation height in desert areas. The results showed that ICESat-2 photon cloud lidar data are suitable for low and sparse vegetation height investigations in desert ecosystems.

In Sentinel-1 terrain observation with progressive scan (TOPS) mode, this azimuth sweeping introduces an extra high-frequency Doppler term into the impulse response function, which requires the azimuth co-registration accuracy of 0.001 pixels to make the interferometric phase difference of adjacent bursts less than 3°. The enhanced spectral diversity (ESD) and developing versions are usually used to correct such residual azimuth misregistration. However, in fast decorrelation scenario, the distributed scatterers (DSs)-based ESDs need to perform lots of complex data processing to reduce the decorrelation impact on high-precision co-registration, including multilooking, spatial filtering, network construction, and least square adjustment. The DS-based co-registration methods are complicated and inefficient, which is not suitable for big SAR data processing. Therefore, this paper proposes a persistent scatterers-based ESD (PS-ESD): to estimate the amplitude dispersion index (ADI) with double samples over the burst overlap regions and select the satisfied PS pixels to compute the double differential phase under the one-master interferometric framework for correcting the time-series azimuth shifts. Based on the Sentinel-1 TOPS SAR data over the mountainous area in Three Gorges, China, experimental results have demonstrated that the proposed PS-ESD can achieve higher co-registration accuracy and faster convergence rate, especially in the case of dynamic co-registration of newly added TOPS images.

A novel method based on covariance vector sparse Bayesian learning (CV-SBL) is proposed in this letter to reconstruct the vertical structure of forests using a small number of synthetic aperture radar (SAR) images. This method regards the inversion of forests reflectivity profiles in the wavelet domain as a sparse signal reconstruction (SSR). Based on the covariance matrix matching criterion, the backscatter power of forests signal and noise will be jointly solved adaptively. Through a few iterations, the exact positions of the closely spaced phase centers can be obtained to simplify the characterization of the vertical structure of the forests. Unlike the traditional compressive sensing (CS) method based on$\ell _{1} $norm convex optimization, the novel method can obtain a real sparse solution without setting hyper-parameters and has a more reliable and accurate reconstruction performance. Besides, the computational efficiency of the new method is much higher than that of the$\ell _{1} $minimization CS method, and it is more suitable for large-scale forests mapping applications. The proposed method is validated using P-band TropiSAR 2009 data set over a test site in Paracou, French Guiana. Furthermore, the reconstruction performance of the proposed method is compared with the spectral analysis methods (Beamforming and Capon) and the$\ell _{1} $minimization super-resolution CS.

Multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the advanced technologies of forest height inversion, as it provides rich observation information. In this letter, we propose a new iterative reweighted method for multibaseline PolInSAR joint inversion of forest height. First, we establish a better stochastic model and weight function to obtain more accurate parameter estimation considering the relationship between vertical wavenumber and forest height. Second, according to the proposed inversion criterion, the baseline observations with unsuitable interferometric geometry are regarded as gross errors and eliminated through reweighted iteration. Finally, we select airborne P-band synthetic aperture radar (SAR) data collected by the F-SAR system during the AfriSAR 2016 campaign for experimental validation. The experimental results show that using initial iteration values obtained by three optimal baseline selection methods, the accuracy of the proposed method achieves 4.47, 4.46, and 4.24 m, which is about 34.74%, 32.32%, and 33.23% higher than those of the existing multibaseline joint inversion method [root mean square error (RMSE) = 6.85, 6.59, and 6.35 m], respectively.

A novel distributed scatterer interferometric synthetic aperture radar (DS-InSAR) method is presented in which the sum of Kronecker product (SKP) decomposition method is applied to DS candidates. Unlike existing polarimetric optimization methods, the proposed method considers polarimetric and interferometric coherence information simultaneously, resulting in separation of the polarimetric scattering process for each target and the maximum diversity for the corresponding phase center locations. Physical reliability of the phase optimization solution is thereby ensured. The performance of the novel method is evaluated using 30 quad-polarized C-band Radarsat-2 synthetic aperture radar (SAR) images over Kilauea Volcano, Hawaii. The proposed method provides a higher density of measurement scatterer (MS) points and a higher quality of DS interferometric phase with a temporally stable phase center than single-polarization (HH) method and quad-polarization exhaustive search polarimetric optimization (ESPO) method. Thus, the proposed method shows good performance in phase quality improvement and point density increasement.

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), launched in May 2019, increased the availability of different types of spaceborne laser altimetry data. But the obtained photon point cloud, especially those for the forest area with steep terrains, contains a lot of background noise that may greatly decrease the accuracy of the extracted digital elevation model (DEM) and forest height. Therefore, removing the background noise photons mixed up with the signal photons is necessary. We proposed a method for photon point cloud filtering using the backward elliptical distance (BED). First, we used the BED to express the spatial distance of the photon point cloud. On this basis, the backward local density was derived to identify signal photons and noise photons. Then we divided the data into several segments and set a local threshold for each segment to identify signal photons and noise photons. We validated the proposed method in the forested area with steep terrains in Washington State and Spain, and compared the results with that of other filtering methods. The comparison shows that the proposed method separates signal photons and noise photons better than other methods. The comprehensive evaluation indexes$F$in Spain and that of the left, center, and right channels in Washington reach 0.9892, 0.9899, 0.9905, and 0.9915, respectively. In addition, compared with the global threshold selection, the local threshold selection is more stable.

Crop rotation is subsidized by the government because of its many advantages. Monitoring whether crop rotation is beneficial for agricultural management, and can also provide a reference for government subsidy policies for crop rotation. In this paper, we propose an unsupervised object-oriented crop rotation detection method using time-series polarimetric SAR (PolSAR) data. On the one hand, we construct the change detection matrix based on the likelihood ratio test (LRT) distance to perform temporal filtering. Then, the pixel-level temporal change image is generated using Shannon entropy and maximum between-class variance (OTSU). On the other hand, we perform temporal segmentation on time-series PolSAR images to obtain superpixel results. Finally, the object-level crop rotation results are obtained with the probabilistic label relaxation (PLR) model. 42 Sentinel-1 dual-polarization SAR datasets during 2018 and 2019 are selected for detecting crop rotation changes on farms within Jinchang, China. Experimental results show that the crop rotation detection accuracy andKappacoefficients of this method can reach 96.21% and 0.8989, respectively.

The superpixel generation is a key step for object-based classification and change detection. For the time-series polarimetric synthetic aperture radar (PolSAR) superpixel generation, the traditional polarimetric similarity measure based on the joint covariance matrix has limitations in discriminating different time-series similarity sequences with different fluctuations. Besides, in the traditional time-series PolSAR superpixel generation methods, it is difficult to determine the tradeoff factor between polarimetric and spatial similarity. In this article, an adaptive time-series PolSAR superpixel generation method based on the simple local iterative clustering (SLIC) is proposed, named time-series polarimetric SAR (TSPol)-adaptive simple local iterative clustering (ASLIC). There are three main improvements. First, a novel time-series polarimetric similarity measure based on the root mean square (rms) is proposed. Multitemporal polarimetric statistical information is combined to describe the polarimetric proximity between pixels, referring to the rms of the multitemporal proximities. Second, an edge detection method based on the stacked 2-D Gaussian-shaped (s2-D GS) window is proposed to initialize the central seeds for superpixel generation. Third, an improved SLIC clustering similarity combined with the time-series polarimetric, time-series power, and spatial similarities is proposed. Meanwhile, a homogeneity factor is applied to adaptively balance the relative weights of various similarities. We use eight Radarsat-2 quad-polarization synthetic aperture radar (SAR) images and 14 Sentinel-1 dual-polarization SAR images to evaluate the effectiveness. The results show our similarity measure and superpixel generation results are superior to those of the traditional methods. For example, as for the Radarsat-2 data, the improvement of the boundary recall by the proposed similarity measure and homogeneity factor is about 4% and 10%, respectively.

Interferometric phase optimization is important key processing for ensuring the application performance of interferometric synthetic aperture radar (InSAR) technology. The noise’s standard deviation depends on the number of looks and the coherence magnitude. Usually, the coherence estimation uses statistical averaging with spatial samples to reduce the speckle noise in interferometric phase images. It has been demonstrated that polarization plays a significant role in the variation of interferometric complex coherence. Currently, InSAR technology utilizes polarimetric information to develop the coherence optimization theory for improving the phase quality. However, the observed coherence region in the complex unitary circle is usually biased from the free-noise one due to the finite multilooking effect and the practical scene heterogeneity, which makes the coherence optimization unstable. In contrast, based on the coherence estimation theory, this article proposes taking polarimetric information as the statistical samples for constructing polarimetric InSAR (PolInSAR) total power (TP) coherency matrix and performs a joint polarization-space nonlocal estimation. Simulated and real experimental results demonstrate that the proposed method improves the performance of the interferometric phase optimization in these three aspects compared with traditional coherence optimization, including phase quality improvement, the number of high coherent points, and computational efficiency.

With the launch of various multipolarimetric satellites, many scholars have introduced the persistent scatterer (PS)-oriented polarimetric optimization methods and extended the persistent scatterer interferometry (PSI) method to multipolarimetric data configuration, called polarimetric PSI (PolPSI) technology. Most PolPSI methods mainly take the amplitude dispersion index (ADI) as the optimization criterion and evaluate the temporal amplitude stationarity of each polarimetric channel for finding an optimal one. However, due to the unstable statistical characteristics of the quality indicator, many non-PS pixels are easily mistaken for the PS candidates (PSCs), and the performance of interferometric phase optimization is also limited. To overcome these restrictions, in this article, a novel PolPSI method is proposed based on the following two improved innovations. First, in terms of PSC selection, the trace moment (TM)-based statistical properties of time-series polarimetric coherency matrices are utilized for selecting the scatterers with the temporal polarimetric stationarity. Second, in terms of interferometric phase optimization, all interferometric coherency matrices of multipolarization channels are added up together to construct the total power (TP) interferogram for suppressing the effect of speckle noise and decorrelation. In the experiment, 13 scenes of quad-polarization ALOS PALSAR-1 image are selected to verify the algorithm’s effectiveness. The experimental results demonstrate that the proposed PolPSI method can better improve the deformation monitoring performance in three aspects than both the single-polarimetric HH and traditional exhaustive search polarimetric optimization (ESPO) methods, including phase quality improvement, density of PSs, and computational efficiency.

The existing local distance statistics-based filtering method for photon point cloud data is greatly affected by the input parameter (number of photon neighbors) and has a poor ability to remove noise photons that are adjacent to signal photons. In this letter, the relative neighboring relationship (RNR) is proposed to describe the relative density distribution of the neighboring photon points around two photon points. The mean local weighted distance is then defined, which is used to enhance the discrimination between the noise photons adjacent to the signal photons and the signal photons. Finally, according to the statistical characteristics of the mean local weighted distance, two strategies for threshold selection are used to separate signal photons from noise photons. ICESat-2 data acquired over tropical forest were used to verify the performance of the proposed method, and the results showed that: 1) the proposed method has a better ability to remove the noise photons adjacent to signal photons and 2) its performance is not greatly dependent on the input parameter.

The potential of interferometric synthetic aperture radar (InSAR) for subsurface height estimation has long been recognized; however, this method is greatly limited by the data sources and the various errors encountered in a highly dynamic environment such as a desert. In this letter, a coherence scattering model based on the volume coherence and imaging geometry of the InSAR acquisitions is proposed to retrieve the penetration depth of the synthetic aperture radar (SAR) signal in a hyperarid desert area. The proposed method includes two main parts: 1) the dielectric constant of the study area is first derived by employing an empirical model with the L-band SAR data, and then, the results are used to calibrate the vertical effective wavenumber after the refraction process and 2) together with the extracted volume coherence from the SAR data, the scattering model is employed to retrieve the penetration depth. The application scope of the vertical effective wavenumber in the volume and temporal decorrelation effect of the model is also discussed in this letter. The method was tested with the Advanced Land Observing Satellite-1 (ALOS-1) Phased Array-type L-Band Synthetic Aperture Radar (PALSAR) data from a desert area in southeast Libya. The results show that the average penetration depth of the L-band SAR in the study area is 2.98 m, and the standard deviation is 1.06 m.

The traditional nonlocal estimations have been demonstrated to be effective and widely used in polarimetric synthetic aperture radar interferometry (PolInSAR) data. However, there still exist some problems about two key steps: 1) in the homogeneous pixels selection step, the regular square (RS) patches matching strategy shows the limited performance in textured area and 2) in the central pixel value estimation from the selected pixels, the well-known Lee estimator, which only uses the intensity statistic, tends to be unstable. To overcome these restrictions, we put forward two robust strategies and then propose an improved PolInSAR complex coherence nonlocal estimation: 1) the shape-adaptive (SA) patch is utilized for flexibly matching the similar pixels in a large search window, which is constructed by combining the likelihood ratio test (LRT) and the region growing (RG) algorithm and 2) the trace-moment-based nonlocal reduced bias (TMB-NLRB) estimator is employed, which considers the interchannel correlations and evaluates more accurately the homogeneity level between the selected pixels. The denoising effect of both strategies is quantitatively analyzed on the simulated data set, and the proposed algorithm is compared with classical estimation algorithms on a TerraSAR-X/TanDEM-X PolInSAR data set. These experimental results show that the proposed method provides better performance in speckle reduction, detail preservation, and complex coherence estimation.

Recent advances in device-free wireless sensing have created the emerging technique of device-free human gesture recognition (DFHGR), which could recognize human gestures by analyzing their shadowing effect on surrounding wireless signals. DFHGR has many potential applications in the fields of human-machine interaction, smart home, intelligent space, etc. State-of-the-art work has achieved satisfactory recognition accuracy when there are a sufficient number of training samples. However, it is time consuming and labor intensive to collect samples, thus how to realize DFHGR under a small training sample set becomes an urgent problem to solve. Motivated by the excellent ability of the generative adversarial network in synthesizing samples, in this article, we explore and exploit the idea of leveraging it to realize virtual samples augmentation. Specifically, we first design a single scenario network with new architecture and better-designed loss function to generate virtual samples using a few number of real samples. Then, we further develop a scenario transferring network to generate virtual samples by utilizing the real samples not only from the current scenario but also from another available scenario as well, which could improve the quality of synthesized samples with the extra knowledge learned from another scenario. We design an mmWave-based DFHGR testbed to test the proposed networks, extensive experimental results demonstrate that the augmented virtual samples are of high quality and facilitate DFHGR systems to achieve better accuracy.

Multitemporal Sentinel-1 data sets are suitable for high-precision agricultural classification mapping due to its short revisit period and dual-polarization channels. At present, more and more attention has been paid to the multitemporal classification methods with feature curve matching, because the time-varying polarimetric characteristics show great potential to crop classification. However, current methods only use the variation of single intensity feature, and the indicators for evaluating similarity have not considered the effect of the variable growing seasons of different parcels. Based on this, a new method with feature curve matching is proposed, which uses the combination of multiple features and applies the discrete Fréchet distance and the Pearson distance to evaluate the similarity between two curves. The proposed method applies time-series Sentinel-1 images for crop classification in two study areas of Gansu province, China. The results show that the overall accuracies in two study areas of the proposed method are 94.98% and 90.20%, respectively. This method achieves higher classification accuracies, compared with the SVM classification method and some other methods with feature curve matching.

Polarimetric synthetic aperture radar interferometry (PolInSAR) and light detection and ranging (LiDAR) have their own respective advantages and disadvantages in extracting large-scale forest height. In this letter, we present an advanced approach to obtain forest canopy height by combining these two strategies. More specifically, the novelty of the proposed method focuses on a dual-baseline selection from multibaseline PolInSAR data, which ensures the robust performance of the forest height inversion by effectively improving the estimation of volume-only coherence. The dual-baseline selection can be regarded as a supervised classification problem. We consider support vector machine (SVM) as an appropriate classifier, and a small amount of sparse LiDAR samples within the coverage of the PolInSAR data (less than 1%) are chosen to assist with the training of the dual-baseline combination classification, which can be met by the current spaceborne LiDAR missions. Finally, we validate the proposed approach by airborne P-band synthetic aperture radar (SAR) data acquired by the F-SAR system and LiDAR data acquired by the National Aeronautics and Space Administration (NASA) Land, Vegetation, and Ice Sensor (LVIS) during the 2016 AfriSAR campaign. The estimation accuracy of the proposed method [$R^{2} = 0.73$ , root-mean-square error (RMSE) = 3.17 m] is 25.24% higher than that of the existing SVM fusion approach devoted to single-baseline selection ($R^{2} = 0.59$ , RMSE = 4.24 m).

Due to the limited accuracy of the current navigation systems, uncompensated motion errors during airborne synthetic aperture radar (SAR) preprocessing, i.e., the residual motion error (RME), cause undesirable phase errors in the final interferogram. Especially in airborne repeat-pass interferometric SAR (InSAR), the removal of RME is critical for topographic mapping. In this letter, based on the geometry of a single-baseline interferogram, a model is first developed for describing the relationship between the time-varying baseline parameters and the interferometric phase errors. A robust estimation is then employed to estimate the RME-induced phase errors. The performance of the proposed method was validated by the use of P- and L-band single-baseline interferograms acquired by the airborne E-SAR system. The results showed that the phase artifacts in the initial differential interferograms can be greatly mitigated. In addition, the corrected interferograms acquired in the P- and L-bands were used to estimate the digital elevation model (DEM). After correction, the root-mean-square errors (RMSEs) of the two DEMs with respect to the light detection and ranging (LiDAR) DEM were 2.6 and 4.6 m, respectively, which are improvements of 48.0% and 63.8%. Furthermore, even in the case of low coherence, the proposed method can still work well.

A novel method based on the optimal normal matrix constraint and cross-iteration algorithm is proposed in this letter to estimate the forest height using the polarimetric interferometry synthetic aperture radar (PolInSAR) data. First, to avoid the null ground-to-volume ratio assumption of the three-stage method, we use the PolInSAR optimal normal matrix constraint method to find out the pure volume coherence. This method can also provide a more accurate initial value for the least-squares iteration. Second, the cross-iteration is used for the forest height inversion, which provides better selection of the best polarization channel and solves the ill-conditioned matrix in the traditional least-squares iteration algorithm. This new method is validated using the BioSAR 2008 P-band data. The results show that the proposed method achieves an average accuracy of 2.6 m, which is better than that of the three-stage inversion method [root-mean-square error (RMSE) = 5.89 m] and the 6-D nonlinear iteration method (RMSE = 4.42 m).

In this paper, a method for digital elevation model (DEM) extraction over forest areas from single-baseline interferometric synthetic aperture radar (InSAR) data is proposed. The main idea of this method is that some backscattering variations which are linked to the geometrical structures of forest occur during the radar acquisition. The time-frequency analysis is used to retrieve these variations by dividing the synthesized SAR image into multiple SAR images in the Fourier domain called sublook images. Then, by interferometry, the sublook images characterized by the same Doppler bandwidth and acquired from spatially separated locations at either end of a baseline are used to estimate the sublook coherences and the above backscattering variations are converted into the variations of sublook coherences. As a result, the number of InSAR observations can be increased. The sublook coherences are then interpreted by the two-layer vegetation scattering model and are assumed to follow a near-linear relationship in the complex plane. The ground phase can then be estimated by linear regression of the sublook coherences. The performance of the proposed method was validated by E-SAR L- and P-band SAR data acquired over coniferous and tropical forests. For the coniferous scenario, the underlying DEM estimated by the proposed method has a root-mean-square error (RMSE) of 4.39 m, which is slightly less accurate than the DEM (with an RMSE of 4.07 m) derived by the polarimetric line-fit (LF) method, but represents a significant improvement in DEM accuracy over the HH InSAR method. For the tropical scenario, the DEMs derived by the proposed method and the polarimetric LF method are closer to the ground surface than those derived by the HH InSAR method, and their mean ground height difference is 0.62 m. The two experiments confirm that it is feasible to extract a DEM by the proposed method, which has a comparable performance in DEM inversion to the polarimetric LF method and only requires single-polarization InSAR data.

In this paper, we demonstrate that the spaceborne dual-platform TerraSAR-X (TSX) and TanDEM-X (TDX) pursuit monostatic mode full polarimetric (full-pol) synthetic aperture radar (SAR) data with a time lag can be used to monitor maritime traffic. For single polarization (single-pol) SAR data, the performance of vessel velocity estimation is mainly determined by 2-D cross correlation of SAR intensity data. As the sea clutter is changing dynamically during the TSX/TDX data acquisition, the correlation between two dual-platform images decreases significantly. We may get unstable or incorrect estimations of vessel velocity, especially under a higher wind condition. For solving this problem, we propose an object-oriented polarimetric likelihood ratio test (PolLRT) method based on the complex Wishart distribution. The proposed method makes PolLRT statistics of the detected target pixels for eliminating the effect of varied sea clutter. Two pairs of full-pol SAR data sets covering the Strait of Gibraltar acquired by dual-platform TSX/TDX in pursuit monostatic mode with a time lag of approximately 10 s are selected for the experiments. The experimental results demonstrate that the proposed PolLRT method has a better performance than that of the classical normalized cross correlation (NCC) method with VV polarization SAR data and the mutual information (MI) method with full-pol SAR data. Specifically, under the lower wind condition, the correct estimation rate of the NCC, the MI, and the proposed PolLRT methods are 85.7%, 57.1%, and 100%, respectively; under the relatively higher wind condition, the correct estimation rate of the above three methods are 48.8%, 23.2%, and 90.1%, respectively.

This letter proposes a modified general polarimetric model-based decomposition method which includes a simplified Neumann volume scattering model (SNVSM). This is useful to avoid a known limitation in one of the state-of-the-art general model-based decomposition methods (i.e., Chen's method), which considers only four possible discrete volume scattering models. Two types of SNVSM, assuming horizontal or vertical dipoles, are derived from the Neumann volume scattering model. The resulting volume coherency matrix exhibits a continuous range of volume scattering models. In addition, this volume model covers both random and nonrandom volume cases, which are distinguished by a randomness parameter. Monte Carlo simulations are used to test this approach. The proposed method with SNVSM overall improves the final accuracy of estimated parameters in comparison with the original approach and shows consistency with another existing generalized volume scattering model (GVSM). In addition, results from two fully polarimetric C- and L-band AIRSAR images over San Francisco region show that the proposed method produces reasonably physical results and outperforms the traditional Y4R method. Finally, the differences obtained between SNVSM and GVSM in two building areas show the potential advantage of SNVSM in identifying more types of volume scenes than that of GVSM.

Compensating the residual motion error (RME) is very important in airborne interferometric synthetic aperture radar (InSAR). In this paper, the wavelet decomposition and polynomial fitting-based (WDPF) method is proposed for detecting and correcting the RME. Wavelet decomposition with root-mean-square error (RMSE) change ratio-based decomposition scale identification is used to detect the RME from the differential interferogram. Polynomial fitting in combination with robust estimation-based least squares is used to absorb the incidence-angle-dependent and topography-dependent components of the RME. A simulated experiment was conducted to test the proposed WDPF method. High-precision RME (with an RMSE of 0.0375 rad) was obtained, which can meet the requirements of InSAR. Real-data L- and P-band InSAR experiments were also performed to test the WDPF method. The results confirmed that the WDPF method can effectively correct the RME for the interferogram. The RMSE of the estimated digital elevation model (DEM) was reduced from 8.03 to 3.46 m and 8.18 to 3.10 m for the L- and P-band interferograms, respectively. Finally, the effects of the external DEM error and polarization on the RME calibration were investigated. The results indicated that the global InSAR DEM products can fulfill the requirement of differential interferogram generation for the WDPF method, and the multipolarization interferograms can help to reduce the effect of the topographic error phase on RME estimation.

This paper presents a wavelet decomposition-based correlation analysis (WDCA) method to correct atmospheric effects for interferometric synthetic aperture radar interferometry. The main idea is based on thea prioriknowledge that the atmospheric effects are independent of the polarizations. This provides the possibility to find the identical atmospheric phases (ATPs) from the two different polarimetric interferograms. To achieve this goal, differential interferometry is performed with different topographic data so that the obtained differential interferograms (D-Infs) have different topographic errors. A polynomial incorporating topographic information is then used to remove the orbit error phase. Thus, the ATPs are the only identical components in the obtained D-Infs. A forward wavelet transform is then utilized to perform multiresolution analysis for the two obtained D-Infs. After this, we apply correlation analysis to identify the wavelet coefficients attributed to the atmospheric effects. The corrected D-Infs are then obtained by down-weighting the wavelet coefficients during inverse wavelet transform. The performance of the WDCA method was tested with L-band ALOS-1 PALSAR dual-polarization SAR images acquired over Southern California and Qilian mountain test sites characterized by different topographic conditions. For the Southern California test site, two interferometric pairs with long and short baselines (750 and 50 m) were formulated. The results show that the WDCA method can work well for both of the interferometric pairs, and the root-mean-square errors (RMSEs) of the obtained DEMs with respect to the Shuttle Radar Topography Mission digital elevation model (DEM) are 7.86 and 13.78 m, and show a decrease of 34.7% and 80.4% for the long- and short-baseline cases, respectively. For the Qilian mountain test site, the corrected interferogram can provide a DEM with an RMSE of 19.73 m, which is an improvement of 22.3% with respect to the DEM containing the atmospheric signals. In addition, the above two experiments show that compared with the existing topographic information-based wavelet method, this approach can remove not only the topography-dependent ATP but also the turbulent ATP.

SAR image from sea can constantly contain ships and their ambiguities in azimuth and range directions. For maritime applications, the ambiguities are visible due to their strong intensities in a low backscattering background of sea environment. Thus, the ambiguities can be often mistaken as ships and cause false alarms. Many approaches have been proposed for reducing the azimuth ambiguities in single channel SAR image. This paper analyzed scattering mechanisms of the azimuth ambiguities for PolSAR images and proposed a method for detecting ships from PolSAR images. By using eigenvector-eigenvalue decomposition, three eigenvalues can be used to differentiate ship targets, azimuth ambiguities and sea clutter. One C-band JPL AIRSAR polarimetric data have been chosen to evaluate the method. The experimental results show that the proposed method can effectively reduce false alarms caused by the azimuth ambiguities.

An innovative constant false alarm rate (CFAR) algorithm was studied for ship detection using synthetic aperture radar (SAR) images of the sea. Two advances were achieved. An alpha-stable distribution rather than a traditional Weibull or$K$-distribution was used to model the distribution of sea clutter. The distribution of sea clutter in a SAR image was typically heterogeneous, caused mainly by variable wind and current conditions. Image segmentation was carried out to improve the homogeneity of the distribution in each subimage or region. In comparison with ship detection using the CFAR algorithms based on the Weibull or$K$-distribution, our algorithm detected the most number of ships with the smallest number of false alarms.