With the advancement of China's satellite technology, the HuanJingJianZai-2 A/B (HJ-2 A/B) satellites, equipped with whisk-broom infrared sensors, represent a significant leap forward in environmental monitoring and Earth observation capabilities. This technological leap, however, introduces new challenges in calibration. The unique structure of the HJ-2 A/B infrared spectroradiometer (IRS) necessitates innovative calibration techniques, as traditional methods primarily focused on exterior orientation parameters (EOPs) and often overlooked the importance of interior orientation accuracy, which is essential for accurate multispectral band registration and color rendering. Addressing this gap, we introduce an innovative multi-focal-plane-array joint calibration method specifically designed for whisk-broom cameras. Our method involves selecting a master band from each focal plane array for accurate focal length calibration and deriving ground control points from image matching and altitude interpolation for comprehensive bundle adjustment. This adjustment refines EOPs and interior orientation parameters (IOPs), ensuring globally optimal EOPs and enhanced IOPs calibration stability. The application of our method to the HJ-2 A/B IRS yielded substantial improvements in georeferencing and band registration accuracies, surpassing traditional methods. This paper details the multi-focal-plane-array joint calibration method, describes the IRS and experimental setup, presents the experimental results, and concludes with the implications and potential applications of our findings.

The exterior orientation parameters (EOPs) provided by the self-developed position and orientation system (POS) of the first Chinese airborne three-line scanner mapping system, AMS-3000, are impacted by jitter, resulting in waveform distortions in rectified images. This study introduces a Gaussian Markov EOP refinement method enhanced by cubic spline interpolation to mitigate stochastic jitter errors. Our method first projects tri-view images onto a mean elevation plane using POS-provided EOPs to generate Level 1 images for dense matching. Matched points are then back-projected to the original Level 0 images for the bundle adjustment based on the Gaussian Markov model. Finally, cubic spline interpolation is employed to obtain EOPs for lines without observations. Experimental comparisons with the piecewise polynomial model (PPM) and Lagrange interpolation model (LIM) demonstrate that our method outperformed these models in terms of geo-referencing accuracy, EOP refinement metric, and visual performance. Specifically, the line fitting accuracies of four linear features on Level 1 images were evaluated to assess EOP refinement performance. The refinement performance of our method showed improvements of 50%, 45.1%, 29.9%, and 44.6% over the LIM, and 12.9%, 69.2%, 69.6%, and 49.3% over the PPM. Additionally, our method exhibited the best visual performance on these linear features.

It is crucial to calibrate the camera’s intrinsic orientation elements and distortion parameters to ensure the photogrammetric accuracies. However, using nadir images to perform this task often leads to correlation between the intrinsic and extrinsic orientation elements, which will result in different camera calibration results by using different self-calibration strategies. It even has an impact on the follow-up processes and makes the product accuracy declined. To overcome this challenge, a robust camera calibration method based on circular oblique images was developed in this study. Firstly, circular oblique images with different viewing angles and camera distances were captured by unmanned aerial vehicle, following a specially designed circular flight path. Then the camera parameters were solved through the self-calibration bundle adjustment based on the circular oblique images. The experiments were carried out to compare the robustness and accuracy of nadir-image-based and circular-oblique-image-based methods. The standard deviation of focal lengths solved by different self-calibration strategies reduced from 12.99 pixels to 1.72 pixels, proving that the proposed method weakens the correlation between the intrinsic and extrinsic orientation elements and has strong robustness. The accuracy of aerial triangulation based on the camera parameters solved by the proposed method improved from 34.7 cm to 3.5cm, illustrating the effectiveness of the proposed method.

The equator’s unique combination of high humidity and temperature renders optical satellite imagery highly susceptible to persistent cloud cover. In contrast, synthetic aperture radar (SAR) offers a robust alternative due to its ability to penetrate clouds with microwave imaging. This study addresses the challenges of cloud-induced data gaps and cross-sensor geometric biases by proposing an advanced optical and SAR image-matching framework specifically designed for cloud-prone equatorial regions. We use a prompt-driven visual segmentation model with automatic prompt point generation to produce cloud masks that guide cross-modal feature-matching and joint adjustment of optical and SAR data. This process results in a comprehensive digital orthophoto map (DOM) with high geometric consistency, retaining the fine spatial detail of optical data and the all-weather reliability of SAR. We validate our approach across four equatorial regions using five satellite platforms with varying spatial resolutions and revisit intervals. Even in areas with more than 50 percent cloud cover, our method maintains sub-pixel edging accuracy under manual check points and delivers comprehensive DOM products, establishing a reliable foundation for downstream environmental monitoring and ecosystem analysis.

Accurately and efficiently identifying rock mass structural planes and extracting their characteristic information is crucial for rock mass stability assessment. Three-dimensional (3D) laser scanning technology can significantly enhance both the efficiency and accuracy of structural plane surveying; however, current mainstream point cloud segmentation algorithms exhibit notable shortcomings, including blurred recognition of structural plane edges, insufficient segmentation accuracy, and poor integration precision among segmented blocks. To address these problems, this study proposes an improved multi-rule region growing point cloud segmentation method for rock structural planes. Specifically, plane fitting residuals are calculated from the point cloud data, and these residual values are then used to optimize seed point selection, thereby improving the segmentation accuracy of planar point sets. Next, considering the spatial relationship between the location of rock structural plane point clouds and their neighborhoods, a KD-tree data structure is employed to perform voxel downsampling for nearest-neighbor searching, and the RANSAC-based region growing algorithm is further refined. By adjusting the region growing segmentation parameters using multiple feature values and segmenting structural planes based on point cloud normal vector differences and final feature values, the proposed method facilitates the extraction of structural plane orientation, spacing, and extent, improving the overall segmentation quality. Experimental results demonstrate that the error between the segmented rock structural plane area and dimensions obtained by this method and those computed using CAD is only 1.07%, which meets the engineering error tolerance. Consequently, the proposed method provides stable and effective technical support for the identification and segmentation of rock structural planes.

High-resolution satellite stereo matching is fundamental for 3D mapping. While current deep learning models achieve impressive macroscopic geometric accuracy, they inherently rely on convolutions with static receptive fields. This rigidity struggles with extreme scale variations across complex landscapes, often causing pervasive localized matching errors, such as edge-fattening artifacts at object boundaries. Furthermore, applying standard dynamic convolutions to address this within massive 4D cost volumes incurs prohibitive memory overhead. To address this trade-off, we propose a parameter-efficient, scale-adaptive cost aggregation strategy. We introduce a Multi-scale Dynamic Receptive Field (MDRF) mechanism that decouples the learned base operator from the dilation-dependent sampling support. By reusing a single shared kernel across multiple dilations via a content-aware attention gate, the network adaptively weights multi-scale context responses. This plug-and-play module is parameter-efficient, with memory and throughput explicitly reported in the evaluated configurations. Evaluated on the WHU-Stereo and US3D benchmarks, our approach consistently lowers the outlier rate, achieving absolute D1-error reductions of 0.43 and 0.49 percentage points, respectively, compared with the baseline. Error-map analysis shows that MDRF reduces localized boundary artifacts, supporting a measured accuracy-efficiency trade-off for practical remote sensing applications.