Pomegranate harvesting remains a challenging task due to the fruit's tough stem, dense canopy, and sensitivity to mechanical damage. Traditional harvesting robots rely on vision-based stem localization, which increases computational complexity and reduces robustness in unstructured orchard environments. This paper presents a dual-shear ring end-effector designed to eliminate the need for precise stem detection, utilizing a self-locking shear mechanism that allows the stem to naturally align between the cutting blades. The system integrates a vision-assisted robotic manipulator for fruit detection and a torque regulation mechanism for optimized cutting force application. Experimental validation demonstrates a success rate of over 90% for stems up to 8 mm in diameter and robust performance even under partial and full occlusion conditions. The results confirm that the proposed system achieves efficient, adaptable, and damage-free harvesting, providing a viable solution for autonomous pomegranate harvesting.

This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise and inference mitigation, while the subsequent Hankel-DMD stage enables reliable parameter extraction even for overlapping sginals or strong interference conditions. Numerical simulations demonstrate the robustness of MAHankDMD, showing its ability to precisely estimate both DOA and frequency under challenging conditions involving RFI and Gaussian white noise interference. The proposed algorithm thus provides an effective solution for channel parameter estimation in complex noisy environments.

This article introduces a recursive parallel dynamic mode decomposition (RPDMD) scheme tailored for multidimensional harmonic retrieval (MHR), specifically applied to MIMO wireless channel sounding. The RPDMD algorithm is devised to address the complexities inherent in multidimensional scenarios, leveraging the dynamic mode decomposition (DMD) framework within a recursive parallel structure. Initially, the observed tensorial multidimensional harmonic data is transformed into a 2-D matrix format along the rth dimension. Subsequently, DMD dissects this matrix data into eigenvalues and their associated modes. The real and imaginary components of the DMD eigenvalues yield damping factors and frequencies in the rth dimension, respectively. Furthermore, recursive DMD is employed to scrutinize each mode independently for parameter retrieval across the remaining dimensions, enabling parallel analysis. Ultimately, this high-dimensional correlated decomposition scheme delivers paired damping factors and frequencies for all tones. Notably, the proposed approach can ascertain the number of tones in undamped sinusoidal signals, making it particularly suitable for MHR even without prior knowledge of the source count. Numerical experiments demonstrate the accuracy and robustness of the RPDMD scheme, with comparative analysis indicating that RPDMD outperforms similar methods, achieving optimal results with minimal mean square error in high signal-to-noise ratio scenarios. This work presents an effective data-driven solution for the MHR problem in MIMO wireless channel sounding.

Landslide is a common geological disaster, and rapid landslide extraction using high-resolution remote sensing imagery (RSI) is of great significance for emergency rescue and damage assessment. In RSI, landslides often have irregular shapes, large-scale variations, and are easily affected by environmental factors. Existing deep learning methods have limited ability in extracting multiscale features, integrating these features effectively, and adapting to complex environments, resulting in models that are not optimized for robustness. To overcome these challenges, this study proposes a spatial context-guided calibration network (SCGC-Net) for multisource remote sensing data. SCGC-Net introduces a novel combination of hybrid multiscale feature extraction, context-aware modulation of landslide characteristics, and a progressive feature calibration fusion strategy, enabling efficient feature extraction, accurate feature integration, and enhanced cross-domain generalization when working with multisource remote sensing data. SCGC-Net was tested on several datasets representing diverse geographical regions and imaging platforms, including the CAS Landslide Dataset (CLD), HR-GLDD, Bijie, and global very-high-resolution landslide mapping (GVLM). Experimental results indicate that SCGC-Net outperforms existing methods across all evaluation metrics and exhibits superior generalization performance in domain adaptation experiments.

Landslides are among the most common natural disasters globally, posing significant threats to human society. In recent years, deep learning (DL) has been widely applied to rapid landslide detection tasks. However, large-scale, multi-area, and multi-sensor landslide datasets for DL landslide detection are still relatively scarce. Most existing datasets adopt a fixed patch size, overlooking the variations in spatial resolution and landslide scale in remote sensing images, thereby limiting the performance of DL models. To address these limitations, we construct a Large-scale Multi-source High-resolution Landslide Dataset (LMHLD). LMHLD collects remote sensing images from five different satellite sensors, covering seven study areas around the world. LMHLD comprises 25,365 image patches of varying sizes and includes 32,296 annotated landslide instances across diverse geographical environments. Additionally, we propose a Semi-Adaptive Patch Size Selection method (SAPSS), which adaptively selects optimal patch sizes for different study areas. Furthermore, we design a training module, LMHLDpart, which enables the seamless integration of multiple heterogeneous sub-datasets within LMHLD, thereby enhancing the flexibility and robustness of DL models trained on LMHLD. Finally, we demonstrated in four evaluation experiments that LMHLD has the potential to become a benchmark dataset for landslide detection. LMHLD provides a strong foundation for DL models, accelerates the development of DL in landslide detection, and serves as a valuable resource for landslide prevention and mitigation efforts. LMHLD is open access and can be accessed through the link: https://doi.org/10.5281/zenodo.11424987.

Coal mining activities can induce ground subsidence, collapse, and even surface fissures, posing a severe threat to human safety. In this article, a novel method integrating interferometric synthetic aperture radar (InSAR) and convolutional neural networks (CNNs) was proposed for automated subsidence funnel identification. Initially, a hybrid InSAR dataset was constructed by combining real samples from mining areas obtained through interferometric processing with simulated samples synthesized using the probability integral method, Polin noise, and complex Gaussian white noise. Subsequently, on the basis of the YOLOv8 algorithm, the adaptive detection head dynamic head (Dyhead) based on attention mechanism and the regression box loss function Wise-intersection over union (WIoU) that can improve the problem of uneven sample difficulty were introduced, resulting in the proposed Improved-YOLOv8 model. Trained on the hybrid dataset, it significantly improved detection accuracy compared to five base models, achieving AP50, AP75, and AP50-95 of 92.0%, 60.0%, and 54.1% respectively. Further experiments and analyses indicate that the trained Improved-YOLOv8 model exhibits satisfactory applicability and accuracy for different surface types and other satellite datasets, and performs well in subsidence funnels detection task covering the entire Shanxi. Therefore, the proposed method shows significant application potential in determining the location distribution of subsidence funnels over wide areas, regularly updating data and monitoring geological disasters in mining areas.

Superpixel analysis is showing great potential for high-resolution synthetic aperture radar (SAR) image change detection, as it uses a larger detection granularity and enhances computational efficiency. However, some deficiencies still exist. It is difficult for previous methods to extract the complete change regions from cluttered backgrounds. Meanwhile, the local spatial correlation and structure consistency are not well represented. To address the above problems and achieve better separation of changed and unchanged superpixels in complicated scenarios, we design a novel unsupervised change detection framework from the perspective of low-rank matrix decomposition (LRMD) theory. The entire framework is carried out in two stages. Firstly, the ℓ1-norm sparsity constraint LRMD model is constructed to decompose change features into a low-rank component associated with background and a sparse component representing changed regions. Then, the local spatial correlation and structure consistency constraint are explicitly modeled by introducing a Laplacian regularization term. The unified model smooths the local similarity superpixels and enlarges the distance between changed regions and the background in the feature subspace. In this stage, the saliency difference image (DI) is generated to indicate the change probabilities of each superpixel. Furthermore, a classification refining module is designed to learn the projection from the change feature matrix to the saliency DI, which can further fine-tune such obscure regions and boost the binary classification. Extensive experiments on five challenging datasets from the TerraSAR-X sensor demonstrate the effectiveness and superiority of the proposed method.

Global navigation satellite system-based bistatic synthetic aperture radar interferometry (GNSS-based InBSAR) suffers from low image resolution, low signal-to-noise ratio (SNR), and accumulated satellite baselines because navigation satellites are used as transmitters. Therefore, traditional texture-based and leader image fixed image registration and persistent scatterer (PS) point selection algorithms cannot be adopted. In this article, a sequential image registration algorithm is proposed based on the preselected PS (prePS) point association for GNSS-based InBSAR systems. First, the prePS points are selected based on the coherence coefficient between theoretical and actual resolution cells. Then, image registration is achieved through prePS point association and subresolution offset estimation, and sequential registration is adopted to replace traditional leader image fixed registration. Finally, PS point selection and interferometric phase extraction are implemented based on the analysis of the resolution cells. Raw data from BeiDou navigation satellites are used to indicate the effectiveness of the proposed algorithm in GNSS-based InBSAR.

As a supplement to time-series interferometric synthetic aperture radar (TS-InSAR), time-series multiple-aperture InSAR (TS-MAI) can measure the spatiotemporal changes in SAR along-track surface deformation. TS-MAI is often applied with low-frequency SAR data (e.g., L-band data) due to its ability to retain high interferometric coherence. However, the low-frequency SAR signal is vulnerable to ionospheric delays, which can significantly degrade the measurement accuracy of TS-MAI. This letter presents an approach to correct the ionospheric errors in TS-MAI. A polynomial cubic model is employed to constrain the ground deformation, which is then incorporated into the observation model for effectively separating the deformation signal and the ionospheric delays. The proposed method is tested using the L-band ALOS-1 PALSAR-1 datasets covering the Tocopilla area in Chile between November 2007 and March 2011. The correction performance and accuracy of the proposed method are demonstrated by comparing the range split-spectrum interferometry (RSSI)-based method and the local GPS data, respectively. The root mean square error (RMSE) improvement rates between TS-MAI and GPS are 72.17% for the SRGD site and 84.51% for the VLZL site, and their correlation coefficients increase from 0.23 and 0.50 to 0.52 and 0.61 after the correction.

Change detection has played an increasingly important role in multitemporal remote sensing applications recently. Long time series analysis is providing new information of land cover changes and improving the quality and accuracy of the change information being derived from remote sensing. The purpose of this study is to dig for more change temporal information and change pattern information from synthetic aperture radar (SAR) image time series (ITS), which is of great significance for monitoring urban area changes, conducting land use surveys, and renovating illegal constructions. In the study, a novel unified framework for long time series SAR image change detection and change pattern analysis (SAR-TSCC) was proposed for land cover change mapping. To obtain the most notable change time rapidly, a fast SAR ITS change point search method based on pruned exact linear time (SAR-PELT) algorithm was adopted. Meanwhile, the deep time series classification network, named SAR time series transformer (SAR-TST), was implemented to recognize the change patterns, which is based on time series transformer (TST) architecture. Considering the lack of real training data, a novel synthetic data generation method is developed. The combination of the synthetic and real data enhanced the generalization of the classifiers. The proposed framework was used for monitoring a large urbanization area in the northwest of Hong Kong, China. The Cosmo Skymed (CSK) time series data acquired from 2013 to 2020 were exploited for land cover change analysis. Experiment results showed that our approach achieved the state-of-the-art performance, as the time accuracy reached 86% and the classification accuracy on the four main change patterns (impulse, step, cycle, and complex) is over 99%. In particular, the proposed SAR-TST model showed remarkable advantages in the presence of insufficient real data.

Ionospheric phase delay is a critical error source in Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) for the purpose of monitoring ground surface deformation with SAR data obtained from low-frequency radar systems. Recently, the Range Split-Spectrum Interferometry (RSSI) method has been employed to estimate and rectify ionospheric errors in TS-InSAR. However, the performance of the RSSI method is largely restricted by the significant linear scale factors resulting from the current small SAR bandwidth. In this study, we propose a Reformulating RSSI (Re-RSSI)-based method for correcting the ionospheric error in TS-InSAR by optimizing the linear scale factors, with the aim of improving the accuracy of TS-InSAR measurements. We evaluate the Re-RSSI method using 121 ALOS-1 PALSAR images that cover two distinct regions: the low-latitude Lazufre volcano region and the high-latitude Anaktuvuk River tundra fire region. Our results demonstrate that the Re-RSSI method can effectively remove time series ionospheric errors at both test sites, where we detected ionospheric delays of approximately 2.5 cm/yr and 2.0 cm/yr, respectively. Using Global Navigation Satellite System (GNSS) measurements as ground truth, we achieved an 86.59% improvement rate in root mean square error (RMSE) with the Re-RSSI method, which is significantly higher than the 66.40% improvement rate achieved with the traditional RSSI method.

Urban impervious surfaces (UIS) indicate the environmental and socioeconomic influences of rapid urbanization. Synthetic aperture radar (SAR) reflects the scattering behaviors of different land covers while multispectral data demonstrate their physicochemical properties. Numerous studies reported that the incorporation of SAR and optical data supplement each other for better extracting UIS, nevertheless, the shadow and layover effects remain unclear, especially in very high-resolution observations. This study analyzed the shadow and layover influences from both optical and SAR data for fine resolution UIS estimation. Given the SAR shadow and layover distribution, we proposed a multisource shadow-based fuzzy set (MSFS) approach for fusing optical and SAR in optical shadow areas using decision fusion. SAR layovers showed effectiveness in UIS extraction. MSFS delivered 3% and 7% improvement in overall accuracy compared with SVM and RF using feature fusion respectively.

Accurate mapping of mangroves species is essential for mangrove management, and deep learning of hyperspectral images (HSIs) shows a great advantage in classification with the fine spectrum. However, the sparely available annotations of HSIs are key challenges for accurate mapping using deep learning, especially for mangrove species within small patches. In this work, a high spatial resolution HSI is synthesized using the method of hyperspectral-multispectral image fusion with spectral variability, providing augmented samples as well as spatial information of mangroves. Secondly, the latest 3D convolutional neural network (3DCNN) was investigated to explore spatial and spectral information for mangrove species mapping. Compared to Gaofen 5 using conventional machine learning methods, the synthetic image provides manyfold samples and higher accuracy for mangrove species mapping using 3DCNNs. This work is expected to improve the situation of sample shortage and spatial information deficiency for mangrove species mapping using deep learning with HSIs.

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) has been used to produce deformation velocity map for investigating the surface subsidence in the rapidly urbanizing metropolitan regions. However, simple analysis techniques like thresholding cannot detect and locate the widely distributed local-scale subsidence reliably. In this study, we propose a deep-learning based method to automatically detect the local-scale subsidence bowls in the deformation velocity map. To test our method, we choose the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the study region, where widespread local-scale subsidence bowls exist associated with the urbanization. Using deformation velocity maps spanning 2015–2017 derived from MT-InSAR, our method detects several subsidence bowls due to dewatering, excavation of foundation pits and subways, and other engineering works. The results demonstrate the potential applicability of the proposed method to automatically detect and analyze the local-scale subsidence bowls in the built-up regions.

High-resolution urban impervious surface (UIS) is essential for social and environmental analysis. However, shadows have become a major challenge to the accurate UIS mapping in high-resolution optical images, as the low reflectance usually leads to misclassification of shadows as roads or waters. To solve this problem, we proposed a shadow free multisource stack sparse autoencoder (ShdFree-MS-SSAE) for urban shadow detection and compensation. Multisource data, including optical, SAR and LiDAR were used for the occlusion information recover. First, MS-SSAE was proposed for urban land cover classification, including shadow and non-shadow area. Then, shadow area in optical data was enhanced with a linear compensation method. Finally, MS-SSAE was applied to classify the enhanced shadow area and the non-shadow area. The results demonstrated that ShdFree-MS-SSAE framework was effective for UIS mapping, with an average improvement of 10%.

Atmospheric artifacts and phase unwrapping errors have unfavorable effects on differential synthetic aperture radar interferometry (DInSAR) deformation monitoring. In this letter, we present an alternative small-baseline DInSAR inversion algorithm, velocity-constraint L1-norm minimization. The proposed algorithm improves the robustness of time series deformation estimation by combining a smoothing constraint and L1-norm minimization. The smoothing constraint can minimize temporal atmospheric artifacts, and the L1-norm minimization outperforms L2-norm minimization in the presence of phase unwrapping errors. The iteratively reweighted least square algorithm is employed to adjust the weights of DInSAR observations and smoothing constraints in L1-norm minimization. The proposed algorithm is validated using simulated data and TerraSAR-X data. The experimental results show that the proposed algorithm is suitable for the inversion of approximately linear deformation processes affected by both atmospheric artifacts and unwrapping errors.

The multispectral instrument (MSI) carried by Sentinel-2A has 13 spectral bands with various spatial resolutions (i.e., four 10-m, six 20-m, and three 60-m bands). A wide range of applications requires a 10-m resolution for all spectral bands, including the 20- and 60-m bands. To achieve this requirement, previous studies used conventional pansharpening techniques, which require a simulated 10-m panchromatic (PAN) band from four 10-m bands [blue, green, red, and near infrared (NIR)]. The simulated PAN band may not have all the information from the original four bands and may have no spectral response function that overlaps the 20- or 60-m bands to be sharpened, which may degrade fusion quality. This paper presents a machine learning method that can directly use the information from multiple 10-m resolution bands for fusion. The method first learns the spectral relationship between the 20- or 60-m band to be sharpened and the selected 10-m bands degraded to 20 or 60 m using the support vector regression (SVR) model. The model is then applied to the selected 10-m bands to predict the 10-m-resolution version of the 20- or 60-m band. The image degradation process was tuned to closely match the Sentinel-2A MSI modulation transfer function (MTF). We applied our method to three data sets in Guangzhou, China, New South Wales, Australia, and St. Louis, USA, and achieved better fusion results than other commonly used pansharpening methods in terms of both visual and quantitative factors.

Phase unwrapping (PU) is one of the key process in reconstructing the digital elevation model (DEM) of a scene from its interferometric synthetic aperture radar (InSAR) data. Compared with traditional single-baseline PU, the multibaseline PU does not need to obey the phase continuity assumption, which can be applicable to reconstruct the DEM where topography varies drastically. However, the performance of the multibaseline PU is directly concerned with noise level. Contrarily, the single-baseline PU algorithm has good noise robustness, since it is based on the globe wrapped phase information, such as PU-max-flow (PUMA) algorithm. In order to improve the noise robustness of the multibaseline, in this paper, we extend single-baseline PUMA algorithm to multibaseline domain, referred to as multibaseline PUMA algorithm, which allows the unwrapping of multibaseline interferograms for the generation of DEM. The proposed algorithm does not need to obey the phase continuity assumption by taking the advantages of multibaseline diversity and improves the noise robustness by using the global wrapped information both from single- and multibaseline domain. The performance of the proposed algorithm is tested on simulated InSAR data experiments, which demonstrate the effectiveness and noise robustness of the proposed algorithm.

In this paper, we extend the previously proposed Tomo-PSInSAR method to detect homogeneous objects in the urban environment. Tomo-PSInSAR integrates conventional persistent scatterer (PS) interferometry and multidimensional SAR tomography to monitor complex built environments [1]. It can jointly detect single and overlaid PSs by constructing a two-tier hierarchical network. Robust estimators (M-estimator and ridge estimator) are introduced to improve the robustness of estimation. To monitor the semi-artificial regions (e.g, pavements and small grassed lands) that are normally distributed scatterers (DSs) in SAR images [2], we analyze homogeneous pixels on the basis of Tomo-PSInSAR. Before estimating the geophysical parameters, we perform a two-sample Anderson-Darling test for the identification of statistically homogeneous pixels at the stage of interferometry. In the first-tier network, the most reliable PSs are identified and they will be used as reference points in the second-tier network. In the second-tier network, the geophysical parameters (e.g., height, deformation velocity) of overlaid PSs are estimated using tomographic imaging [3] and the geophysical parameters of DSs are estimated using the Capon-Beamforming algorithm [4]. The removal of atmospheric delay in the second-tier network is accomplished by subtracting the phase of adjacent PSs that are detected in the first-tier network. In this sense, the proposed integrated method as shown in Fig. 1 can jointly monitor single PSs, overlaid PSs, and DSs according to specific cases. TerraSAR-X/TanDEM-X images are used to validate this method. The results are shown in Fig. 2-4.

In this paper, we develop a SAR tomography-based persistent scatterer interferometry (Tomo-PSInSAR) method to detect single and double persistent scatterers (PSs) in urban built environments. By constructing a two-tier network, we can jointly detect single and double PSs with no need for preliminary removal of the atmospheric phase screen (APS) in the whole area. This technique is more applicable in high-rise built environments (e.g., Hong Kong) with cloudy and rainy weather where there is much uncertainty when removing the APS. In the first-tier network, we aim to detect the most reliable single PSs (SPSs) by constructing a Delaunay triangulation network. To improve the robustness of estimation, we combine beamforming with an M-estimator for parameter estimation at the arcs, and introduce a ridge-estimator for network adjustment. In the second-tier network, we detect the remaining SPSs and all of the double PSs (DPSs) by constructing local star networks that use the SPSs detected in the first-tier network as reference points. To simplify the detection of DPSs, we employ a local maximum ratio (LMR) method for extracting overlaid DPSs. Finally, TerraSAR-X images are used to validate the Tomo-PSInSAR method.

In this paper, we develop a synthetic aperture radar (SAR) tomography-based persistent scatterer interferometry (Tomo-PSInSAR) method to detect single and double persistent scatterers (PSs) in urban built environments. By constructing a two-tier network, we can jointly detect single and double PSs with no need for preliminary removal of the atmospheric phase screen (APS) in the whole area. This technique is more applicable in high-rise built environments (e.g., Hong Kong) with cloudy and rainy weather, where there is much uncertainty when removing the APS. In the first-tier network, we aim to detect the most reliable single PSs (SPSs) by constructing a Delaunay triangulation network. To improve the robustness of estimation, we combine beamforming with an M-estimator for parameter estimation at the arcs and introduce a ridge estimator for network adjustment. In the second-tier network, we detect the remaining SPSs and all of the double PSs (DPSs) by constructing local star networks that use the SPSs detected in the first-tier network as reference points. To simplify the detection of DPSs, we employ a local maximum ratio method for extracting overlaid DPSs. Finally, 56 Hong Kong TerraSAR-X images are used to validate the Tomo-PSInSAR method.

L1 minimization has proven to be useful for tomographic synthetic aperture radar (SAR) imaging because it has super-resolution capability and produces no sidelobes. However, it cannot always derive the sparsest solution and often yields outliers in recovery. Consequently, it is usually difficult to extract true persistent scatterers straightforwardly in practice. To enhance the sparsity, we introduce iterative reweighted L1 minimization for sparse inversion. The weight factor is computed in each iteration, according to the previous tomographic magnitude to establish a more democratic penalization rule. Our simulation results indicate that the reweighted algorithm can achieve perfect recovery when noise is lower. Specifically, when the signal-to-noise ratio is equal to 5 dB, two reweighted iterations can improve the probability of true sparsity from 29.2% to 99.8% for single scatterers and from 0.2% to 95.4% for double scatterers. Due to the enhanced sparsity, we can directly identify scatterers without the need for further model selection. The method is validated using 44 TerraSAR-X/ TanDEM-X images. Single and double scatterers are detected in urban areas. Verification using light detection and ranging (LiDAR) data indicates that we achieve submeter accuracy of the height estimates.

We investigated the joint use of the high-resolution WorldView-2 optical satellite images and the multitemporal TerraSAR-X synthetic aperture radar (SAR) satellite images to extract building height information in high-density urban areas. The main idea of the proposed fusion approach is to take full advantage of both data sets in building height retrieval. The proposed approach includes two main stages. First, initial building height estimates are extracted from WorldView-2 stereo images and multitemporal SAR images. These initial results are then combined using a novel object-based fusion approach, in which the heights of points for the same building footprint are retrieved and integrated. Experiments on the Mong Kok area of Hong Kong showed that the proposed approach using both data sets outperforms the use of either stereo images or SAR images alone. According to the results of the proposed approach, the average absolute height retrieval error is 6.53 m, which is much lower than using stereo and SAR images (9.08 and 12.24 m, respectively). The proposed fusion approach is suitable for building height retrieval in urban areas where single satellite data have limitations.

In this letter, we investigate the polarization coherence tomography technique and propose a new function expansion to reconstruct the vertical profile function. Instead of generating profile in Fourier-Legendre series, we deduce orthogonal functions on [-1, 1] by weight ofz2, which can increase the highest polynomial order and decrease the condition number of the inversion matrix, indicating that the inversion in the new expansion is more stable and less susceptible to noise. Finally, we apply the technique to simulated dual-baseline data and Chinese X-band single-baseline polarimetric synthetic aperture radar interferometry data to demonstrate its validity and robustness.

In this paper we investigate the interferometric coherence optimization methods and develop a new procedure for the solution of the optimum coherence in case of single-mechanism using genetic algorithm. Finally, Chinese X-band airborne PolInSAR data is employed to validate the efficiency.