DSM block adjustment is a crucial step to improve absolute height accuracy and consistency within overlap in large-scale topographic mapping using bistatic interferometric SAR. More objects, fixed error models, and more observations types with varying accuracies pose significant challenges for adjustment. To this end, this letter proposes an integration of iterative method, hypothesis testing and surveying adjustment into the solution. Preconditioning technology and -test are combined for adaptive correcting of the error model, while robust Helmert variance component estimation is employed for dynamic update of the stochastic model. Large-scale spaceborne bistatic SAR data containing diverse land cover types are utilized to validate the method. The 90% linear error (LE90) of DSM height against ICESat-2 decreases from 26.27 m to 4.90 m, corresponding to a decrease from 27.98 m to 3.70 m in LE90 of height difference at tie-points. The results demonstrate not only the ability to obtain the most suitable height error model for each DSM scene, but also the determination of appropriate weighting ratio between control point and tie-point while resisting gross error.

Side‐scan sonar (SSS) detection is a key method in underwater environmental security and subsea resource development. However, many detection approaches primarily concentrate on tracking the evolution path of optical image object detection tasks when using acoustic images, resulting in complex structures and limited versatility. To tackle this issue, we introduce a pioneering dual‐domain multi‐frequency network (D ² MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection. In D ² MFNet, a novel method for optimizing and improving the detection sensitivity in different frequency ranges called multi‐frequency combined attention mechanism (MFCAM) is proposed. This mechanism amplifies the relevance of dual‐domain features across different channels and spaces. Moreover, we introduce a dual‐domain feature pyramid network (D ² FPN) significantly augments the depth and breadth of feature information in underwater small datasets. The methods offer plug‐and‐play functionality with substantial performance enhancements. Extensive experiments are conducted to validate the efficacy of the proposed techniques, and the results showcase their state‐of‐the‐art performance. MFCAM improves the mAP by 16.9% in the KLSG dataset and 15.5% in the SCTD dataset. The mAP of D ² FPN was improved by 8.4% in the KLSG dataset and by 9.8% in the SCTD dataset. The code and models will be publicly available at https://dagshub.com/estrellaww00/D2MFNet .

The precise tie-points (TPs) on synthetic aperture radar (SAR) images are a critical cornerstone in the global digital elevation model (DEM) and digital ortho map (DOM) production process. While there are abundant studies on SAR TPs matching, improvement opportunities persist in large areas. The correspondences have pixel-level errors during geocoding, which result in misalignment between global products. Consequently, this paper proposed a robust method for SAR images TPs matching, which consists of three key steps: (1) interest point extraction based on the dynamic Harris area entropy (DHAE) grid; (2) adaptive determination of template size; (3) normalized cross correlation (NCC) template matching. DHAE is a regional texture information grid based on the SAR-Harris map, and it is achieved through dynamic block division. Generating the DHAE grid over SAR images enables the extraction of interest points that have regional feature representation and distribution uniformity. A variable-size matching template is adaptively determined based on DHAE to enhance template quality while maintaining computational efficiency. Subsequently, the NCC algorithm is employed to find subpixel-precise correspondences. The proposed method is applied on TPs matching in 57 Terra-SAR images, which cover a large geographical area. Furthermore, the overlapping area is partitioned into five segments according to different coverage types. The experimental results demonstrate that the proposed method outperforms other template matching methods. For all coverage types, the proposed method exhibits high-precision sub-pixel results that reach up to 38.64% in terms of the relative positioning error (RPE), particularly in texture-weak and large areas.

Multi-temporal synthetic aperture radar interferometry (SAR, MT-InSAR) has been widely recognized as an effective technique for monitoring surface deformation and marking a significant advancement in satellite geodesy to millimeter-level precision. As one of the most representative MT-InSAR methods, permanent scatterer interferometry (PS, PSI) focuses on the most elite pixels over the temporal and spatial scales of SAR images. The selection of PS points is the cornerstone of the excellent performance of PSI, directly influencing the accuracy and density of surface deformation products. Most traditional methods use thresholds to divide PS and non-PS pixels, and their results will no longer be accurate when the surface deformation patterns deviate from the prior model. Benefiting from the development of deep learning, data-driven methods have been widely proposed in recent years and exhibit superior efficiency. However, existing approaches do not fully exploit the contextual relationships between phase, amplitude, time, and spatial dimensions. This will result in the selected PS points showing representative only in certain dimensions.Therefore, this paper proposed a novel deep learning method for PS selection that leverages the temporo-spatial context features of amplitude images and interferometric phase. Specifically, a pseudo-Siamese temporo-spatial vision transformer (ViT) architecture is employed to process input amplitude and phase time-series stacks simultaneously. In the backbone, the positional information is incorporated into the image tokens via the temporal and spatial embedding layers, and the local features in the context of the time series images are derived by the temporal and spatial encoder. Through a feature fusion module, multi-scale features from amplitude and phase are synergistically integrated. Then, it is output to the decoding head, and the high-quality PS points are predicted pixel by pixel through a multilayer perceptron.The proposed model was trained on a dataset containing time-series SAR amplitude images and interferometric phase stacks of Barcelona, acquired by the TerraSAR between 2009 and 2011. The dataset includes 8,689 samples for training and 965 samples for validation, with data pre-processing and PS annotation performed using the SUBSIDENCE software from the Universitat Politècnica de Catalunya. To address the class imbalance between PS and non-PS points, the focal loss function was employed. The proposed model was evaluated using metrics like intersection over union (IoU), F1-score, precision, and recall. PS points selected by our method are validated via qualitative and quantitative comparisons against other state-of-the-art methods.Experimental results indicate that the proposed method markedly improves the density, precision, and phase integrity of PS points. Compared to traditional methods, the proposed model yields more complete and continuous PS point details on buildings and man-made infrastructure, reduces false points, and improves computational efficiency. Additionally, the proposed method performs robustly across diverse land types and is extendable to distributed scatterer (DS) pixel selection. All model codes and training configurations will be available at https://dagshub.com/zhangyfcsu/pssformer.