Search Results (672)

Polarimetric Synthetic Aperture Radar (PolSAR) image classification is one of the most important applications in remote sensing. The impressive superpixel generation approaches can improve the efficiency of the subsequent classification task and restrain the influence of the speckle noise to an extent. Most of the classical PolSAR superpixel generation approaches use the features extracted manually and even only consider the pseudocolor images. They do not make full use of polarimetric information and do not necessarily lead to good enough superpixels. The deep learning methods can extract effective deep features but they are difficult to combine with superpixel generation to achieve true end-to-end training. Addressing the above issues, this study proposes an end-to-end fully convolutional superpixel generation network for PolSAR images. It integrates the extraction of polarization information features and the generation of PolSAR superpixels into one step. PolSAR superpixels can be generated based on deep polarization feature extraction and need no traditional clustering process. Both the performance and efficiency of generations of PolSAR superpixels can be enhanced effectively. The experimental results on various PolSAR datasets show that the proposed method can achieve impressive superpixel segmentation by fitting the real boundaries of different types of ground objects effectively and efficiently. It can achieve excellent classification performance by connecting a very simple classification network, which is helpful to improve the efficiency of the subsequent PolSAR image classification tasks. Full article

In order to achieve 0.1 m resolution and fully polarimetric observation capabilities for airborne SAR systems, the adoption of stepped-frequency modulation waveform combined with the polarization time-division transmit/receive (T/R) technique proves to be an effective technical approach. Considering the issue of range resolution degradation and paired echoes caused by multichannel amplitude–phase mismatch in fully polarimetric airborne SAR with 0.1 m resolution, an amplitude–phase error estimation algorithm based on echo data is proposed in this paper. Firstly, the subband amplitude spectrum correction curve is obtained by the statistical average of the subband amplitude spectrum. Secondly, the paired-echo broadening function is obtained by selecting high-quality sample points after single-band imaging and the nonlinear phase error within the subbands is estimated via Sinusoidal Frequency Modulation Fourier Transform (SMFT). Thirdly, based on the minimum entropy criterion of the synthesized compressed pulse image, residual linear phase errors between subbands are quickly acquired. Finally, two-dimensional cross-correlation of the image slice is utilized to estimate the positional deviation between polarization channels. This method only requires high-quality data samples from the echo data, then rapidly estimates both intra-band and inter-band amplitude/phase errors by using SMFT and the minimum entropy criterion, respectively, with the characteristics of low computational complexity and fast convergence speed. The effectiveness of this method is verified by the imaging results of the experimental data. Full article

Compared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSAR) image classification tasks. This paper proposes a multi-scale feature extraction (MSFE) method based on a 3D complex-valued network to improve classification accuracy by fully leveraging multi-scale features, including phase information. We first designed a complex-valued three-dimensional network framework combining complex-valued 3D convolution (CV-3DConv) with complex-valued squeeze-and-excitation (CV-SE) modules. This framework is capable of simultaneously capturing spatial and polarimetric features, including both amplitude and phase information, from PolSAR images. Furthermore, to address robustness degradation from limited labeled samples, we introduced a multi-scale learning strategy that jointly models global and local features. Specifically, global features extract overall semantic information, while local features help the network capture region-specific semantics. This strategy enhances information utilization by integrating multi-scale receptive fields, complementing feature advantages. Extensive experiments on four benchmark datasets demonstrated that the proposed method outperforms various comparison methods, maintaining high classification accuracy across different sampling rates, thus validating its effectiveness and robustness. Full article

The coherency matrix serves as a valuable tool for explaining the intricate details of various terrain targets. However, a significant challenge arises when analyzing ground targets with similar scattering characteristics in polarimetric synthetic aperture radar (PolSAR) target decomposition. Specifically, the overestimation of volume scattering (OVS) introduces ambiguity in characterizing the scattering mechanism and uncertainty in deciphering the scattering mechanism of large oriented built-up areas. To address these challenges, based on the generalized five-component decomposition (G5U), we propose a hierarchical extension of the G5U method, termed ExG5U, which incorporates orientation and phase angles into the matrix rotation process. The resulting transformed coherency matrices are then subjected to a five-component decomposition framework, enhanced with four refined volume scattering models. Additionally, we have reformulated the branch conditions to facilitate more precise interpretations of scattering mechanisms. To validate the efficacy of the proposed method, we have conducted comprehensive evaluations using diverse PolSAR datasets from Gaofen-3, Radarsat-2, and ESAR, covering varying data acquisition timelines, sites, and frequency bands. The findings indicate that the ExG5U method proficiently captures the scattering characteristics of ambiguous regions and shows promising potential in mitigating OVS, ultimately facilitating a more accurate portrayal of scattering mechanisms of various terrain types. Full article

Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is difficult to obtain a large number of labeled samples in real oil spill monitoring scenarios. Surprisingly, few-shot learning can achieve excellent classification performance with only a small number of labeled samples. In this context, a new cross-domain few-shot SAR oil spill detection network is proposed in this paper. Significantly, the network is embedded with a hybrid attention feature extraction block, which consists of a coordinate attention module to perceive the channel information and spatial location information, as well as a global self-attention transformer module capturing the global dependencies and a multi-scale self-attention module depicting the local detailed features, thereby achieving deep mining and accurate characterization of image features. In addition, to address the problem that it is difficult to distinguish between the suspected oil film in seawater and real oil film using few-shot due to the small difference in features, this paper proposes a double loss function category determination block, which consists of two parts: a well-designed category-perception loss function and a traditional cross-entropy loss function. The category-perception loss function optimizes the spatial distribution of sample features by shortening the distance between similar samples while expanding the distance between different samples. By combining the category-perception loss function with the cross-entropy loss function, the network’s performance in discriminating between real and suspected oil films is thus maximized. The experimental results effectively demonstrate that this study provides an effective solution for high-precision oil spill detection under few-shot conditions, which is conducive to the rapid identification of oil spill accidents. Full article

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article

Dual polarimetric SAR is capable of reflecting the biophysical and geometrical information of terrain with open access data availability. When it is combined with time-series observations, it can effectively capture the dynamic evolution of scattering characteristics of crops in different growth cycles. However, the actual planting of crops often shows spatial dispersion, and the same crop may be dispersed in different plots, which fails to adequately consider the correlation information between dispersed plots of the same crop in spatial distribution. This study proposed a crop classification method based on multi-temporal dual polarimetric data, which considered the utilization of information between near and far spatial plots, by employing superpixel segmentation and a HyperGraph neural network, respectively. Firstly, the method utilized the dual polarimetric covariance matrix of multi-temporal data to perform superpixel segmentation on neighboring pixels, so that the segmented superpixel blocks were highly compatible with the actual plot shapes from a long-term period perspective. Then, a HyperGraph adjacency matrix was constructed, and a HyperGraph neural network (HGNN) was utilized to better learn the features of plots of the same crop that are distributed far from each other. The method fully utilizes the three dimensions of time, polarization and space information, which complement each other so as to effectively realize high-precision crop classification. The Sentinel-1 experimental results show that, under the optimal parameter settings, the classified accuracy of combined temporal superpixel scattering features using the HGNN was obviously improved, considering the near and far distance spatial correlations of crop types. Full article

Effective soil salinity monitoring is crucial for sustainable land management in arid regions. Most current studies face limitations by relying solely on single-source data. This study presents a novel three-dimensional (3D) optical-radar feature space model combining Gaofen-3 polarimetric synthetic aperture radar (SAR) and Sentinel-2 multispectral data for China’s Yutian Oasis. The random forest (RF) feature selection algorithm identified three optimal parameters: Huynen_vol (volume scattering component), RVI_Freeman (radar vegetation index), and NDSI (normalized difference salinity index). Based on the interactions of these three optimal features within the 3D feature space, we constructed the Optical-Radar Salinity Inversion Model (ORSIM). Subsequent validation using measured soil electrical conductivity (EC) data (May–June 2023) demonstrated strong model performance, with ORSIM achieving R² = 0.75 and RMSE = 7.57 dS/m. Spatial analysis revealed distinct salinity distribution patterns: (1) Mildly salinized areas clustered in the central oasis region, and (2) severely salinized zones predominated in northern low-lying margins. This spatial heterogeneity strongly correlated with local topography-higher elevation (south) to desert depression (north) gradient. The 3D feature space approach advances soil salinity monitoring by overcoming traditional 2D limitations while providing an accurate, transferable framework for arid ecosystem management. Furthermore, this study significantly expands the application potential of SAR data in soil salinization research. Full article

Soil salinization poses a significant threat to agricultural productivity, food security, and ecological sustainability in arid and semi-arid regions. Effectively and timely mapping of different degrees of salinized soils is essential for sustainable land management and ecological restoration. Although deep learning (DL) methods have been widely employed for soil salinization extraction from remote sensing (RS) data, the integration of multi-source RS data with DL methods remains challenging due to issues such as limited data availability, speckle noise, geometric distortions, and suboptimal data fusion strategies. This study focuses on the Keriya Oasis, Xinjiang, China, utilizing RS data, including Sentinel-2 multispectral and GF-3 full-polarimetric SAR (PolSAR) images, to conduct soil salinization classification. We propose a Dual-Modal deep learning network for Soil Salinization named DMSSNet, which aims to improve the mapping accuracy of salinization soils by effectively fusing spectral and polarimetric features. DMSSNet incorporates self-attention mechanisms and a Convolutional Block Attention Module (CBAM) within a hierarchical fusion framework, enabling the model to capture both intra-modal and cross-modal dependencies and to improve spatial feature representation. Polarimetric decomposition features and spectral indices are jointly exploited to characterize diverse land surface conditions. Comprehensive field surveys and expert interpretation were employed to construct a high-quality training and validation dataset. Experimental results indicate that DMSSNet achieves an overall accuracy of 92.94%, a Kappa coefficient of 79.12%, and a macro F1-score of 86.52%, positively outperforming conventional DL models (ResUNet, SegNet, DeepLabv3+). The results confirm the superiority of attention-guided dual-branch fusion networks for distinguishing varying degrees of soil salinization across heterogeneous landscapes and highlight the value of integrating Sentinel-2 optical and GF-3 PolSAR data for complex land surface classification tasks. Full article

Tropical forests harbor a significant portion of global biodiversity but are increasingly degraded by human activity. Assessing restoration efforts requires the systematic monitoring of tropical ecosystem status and recovery. Satellite-borne synthetic aperture radar (SAR) supports monitoring changes in vegetation structure and is of particular utility in tropical regions where clouds obscure optical satellite observations. To characterize tropical forest recovery in the Lowland Chocó Biodiversity Hotspot of Ecuador, we apply over a decade of dual-polarized (HH + HV) L-band SAR datasets from the Japanese Space Agency’s (JAXA) PALSAR and PALSAR-2 sensors. We assess the complementarity of the dual-polarized imagery with less frequently available fully-polarimetric imagery, particularly in the context of their respective temporal and informational trade-offs. We examine the radar image texture associated with the dual-pol radar vegetation index (DpRVI) to assess the associated determination of forest and nonforest areas in a topographically complex region, and we examine the equivalent performance of texture measures derived from the Freeman–Durden polarimetric radar decomposition classification scheme applied to the fully polarimetric data. The results demonstrate that employing a dual-polarimetric decomposition classification scheme and subsequently deriving the associated gray-level co-occurrence matrix mean from the DpRVI substantially improved the classification accuracy (from 88.2% to 97.2%). Through this workflow, we develop a new metric, the Radar Forest Regeneration Index (RFRI), and apply it to describe a chronosequence of a tropical forest recovering from naturally regenerating pasture and cacao plots. Our findings from the Lowland Chocó region are particularly relevant to the upcoming NASA-ISRO NISAR mission, which will enable the comprehensive characterization of vegetation structural parameters and significantly enhance the monitoring of biodiversity conservation efforts in tropical forest ecosystems. Full article

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

The debate surrounding the optimal polarimetric modes—compact polarimetry (CP) versus dual polarization (DP)—for PolSAR ship detection persists. This study pioneers a systematic investigation into Generalized Compact Polarimetry (GCP) for this application. By synthesizing and evaluating 143 distinct GCP configurations from fully polarimetric data, this study presents the first comprehensive comparison of their ship detection performance against conventional modes using Target-to-Clutter Ratio (TCR) and deep learning-based accuracy (AP50). Experiments on the FPSD dataset reveal that an optimized GCP mode (e.g., ellipse/orientation: [−10, −5]) consistently outperforms traditional CP and DP modes, yielding TCR gains of 0.2–2.7 dB. This translates to AP50 improvements of 0.5–4.7% (Faster R-CNN) and 0.1–5.5% (RetinaNet) over five common baseline modes. Crucially, this enhancement arises from optimizing the interaction between the polarization mode and target/clutter scattering characteristics rather than algorithmic improvements, supporting the proposed “optimization from the information source” strategy. These findings offer significant implications for future PolSAR system design and operational mode selection. Full article

This study aims to elucidate the optimal exploitation of polarimetric scattering information in dual-pol SAR data. For an effective comparison of the flood detection performance between dual-pol parameters, we presented a simple fuzzy-based flood detection algorithm. Scattering characteristics of water surface and non-water land can vary depending on the region and flood conditions. Therefore, the flood detection performance of the dual-pol parameters was evaluated across three datasets with different geographic, climatic, and land cover conditions. The results demonstrated that accurate and stable performance in the detection of inundated areas under different surface conditions can be achieved by combining water body information from dual-pol channels in a disjunctive way. It also suggests that synergy in flood detection can be expected when using polarization observation data by considering each polarimetric channel as an independent information source and combining them rather than deriving the most relevant polarization parameter. Furthermore, combining common information from two dual-pol channels in a conjunctive way could provide the most reliable SAR flood detection results with minimum false alarms from the user’s perspective. Based on these experimental results, a two-class flood classification scheme was proposed for improving the applicability of SAR remote sensing in identifying flooded areas. Full article

Limited labels and detailed changed land-cover interpretation requirements pose challenges for time-series PolSAR change monitoring research. Accurate labels and supervised models are difficult to reuse between massive unlabeled time-series PolSAR data due to the complex distribution shifts caused by different imaging parameters, scene changes, and random noises. Moreover, many related methods can only detect binary changes in PolSAR images and struggle to track the detailed land cover changes. In this study, an unsupervised cross-domain method based on limited-label transfer learning and a vision transformer (LLTL-ViT) is proposed for PolSAR land-cover change monitoring, which effectively alleviates the problem of difficult label reuse caused by domain shift in time-series SAR data, significantly improves the efficiency of label reuse, and provides a new paradigm for the transfer learning of time-series polarimetric SAR. Firstly, based on the polarimetric scattering characteristics and manifold-embedded distribution alignment transfer learning, LLTL-ViT transfers the limited labeled samples of source-domain PolSAR data to unlabeled target-domain PolSAR time-series for initial classification. Secondly, the accurate samples of target domains are further selected based on the initial transfer classification results, and the deep learning network ViT is applied to classify the time-series PolSAR images accurately. Thirdly, with the reliable secondary classification results of time-series PolSAR images, the detailed changes in land cover can be accurately tracked. Four groups of cross-domain change monitoring experiments were conducted on the Radarsat-2, Sentinel-1, and UAVSAR datasets, with about 10% labeled samples from the source-domain PolSAR. LLTL-ViT can reuse the samples between unlabeled target-domain time-series and leads to a change detection accuracy and specific land-cover change tracking accuracy of 85.22–96.36% and 72.18–88.06%, respectively. Full article

Existing forest height estimation methods based on polarimetric interferometric synthetic aperture radar (PolInSAR) typically process each pixel independently, potentially introducing inconsistent estimates and additional decorrelation in the covariance matrix estimation. To address these limitations and effectively exploit the spatial context information, this paper proposes the first patch-based inversion method named joint pixel optimization inversion (JPO). By leveraging the smoothness and regularity of homogeneous pixels, a joint-pixel optimization problem is constructed, incorporating a first-order regularization on the ground phase. To solve the non-parallelizable problem of the alternating direction method of multipliers (ADMM), we devise a new parallelizable ADMM algorithm and prove its sublinear convergence. With the contextual information of neighboring pixels, JPO can provide more reliable forest height estimation and reduce the overestimation caused by additional decorrelation. The effectiveness of the proposed method is verified using spaceborne L-band repeat-pass SAOCOM acquisitions and LiDAR heights obtained from ICESat-2. Quantitative evaluations in forest height estimation show that the proposed method achieves a lower mean error (1.23 m) and RMSE (3.67 m) than the existing method (mean error: 3.09 m; RMSE: 4.70 m), demonstrating its improved reliability. Full article