Dochul Yang

Accurate ground elevation beneath forest canopies is important for terrain and forest-structure analysis, yet satellite-based elevation data can be affected by canopy and terrain conditions, while spaceborne light detection and ranging (LiDAR) provides ground elevations only at discrete locations. Here, the elevation offset between the Copernicus GLO-30 DEM (CopDEM) and Global Ecosystem Dynamics Investigation (GEDI) L2A ground elevation was estimated using an Explainable Boosting Machine (EBM) with topographic, canopy-structure, and CopDEM-uncertainty predictors to generate continuous forest digital terrain models (DTMs) for two regions in the Republic of Korea. Validation against independent airborne LiDAR data showed root mean square errors (RMSEs) of the proposed DTM of 4.10 m in the central-western region and 3.67 m in the eastern region. In the EBM, the CopDEM height error mask (HEM), canopy cover, and aspect contributed most to the estimated offset, and the magnitude and direction of their contributions varied across canopy–terrain conditions and between regions. Residual bias increased with canopy height and became more negative on steep slopes, where RMSE also increased. These results show that the proposed approach can generate continuous forest DTMs while quantifying the contributions of the main predictors to the offset, their variation across environmental conditions, and the conditions under which residual errors increase.

Vendor-provided rational polynomial coefficients (RPCs) for high-resolution optical satellite imagery often contain systematic biases that are commonly corrected using field-surveyed ground control points (GCPs). Acquiring such points can be costly or infeasible in inaccessible regions. This study proposes an RPC bias compensation framework for KOMPSAT-3 (K3) optical imagery based on the geometric accuracy and stability of KOMPSAT-5 (K5) X-band SAR enhanced high-resolution (EH) imagery, without measuring image coordinates of control points. Three-dimensional ground control data (GCD) are projected into both sensor image spaces via the K5 Range–Doppler (RD) model and the K3 RPC model. A GCD-driven projective transformation provides coarse alignment, followed by fine registration using FFT-based phase correlation (FPC). Registration-derived correspondence coordinates are then used to estimate a strongly regularized image-space affine bias model, yielding a predominantly translational correction. Three GCD sources—Korean public control points (PCPs), ICESat-2 altimetry, and the 30 m SRTM DEM—were evaluated using one K3–K5 image pair over Suncheon, Korea. A sensitivity analysis showed that control points on flat terrain (elevation ≤60 m, slope ≤6°) yielded stable results, whereas points on steep or forested terrain degraded the registration, and increasing the number of GCD points did not systematically improve accuracy. Under these conditions, all three sources reduced the RMSE at independent GNSS checkpoints from 7.1 and 10.6 pixels in the column and row directions to 1.6–2.0 and 1.6–2.1 pixels, respectively. This accuracy is comparable to that of conventional GCP-based compensation (1.7/1.3 pixels), whereas two structural-feature-based matching methods (HOPC and CFOG) did not improve on the original RPCs under the tested implementations. These results demonstrate the potential of SRTM-derived control data for meter-level directional refinement of optical imagery when reliable SAR–optical registration is achieved. Generalization beyond the tested image pair requires further validation.

Spaceborne synthetic aperture radar (SAR) needs point-like reference targets for co-registration, calibration, and absolute geolocation, but real corner reflectors (CRs) exist only at a few dedicated sites. We present a detection and three-dimensional (3D) positioning pipeline for naturally occurring corner-reflector-like (pseudo-CR) scatterers in KOMPSAT-5 stacks, combining constant false alarm rate (CFAR) detection ranked by local contrast rather than absolute power, a multi-scale template bank, multi-look range-Doppler bundle triangulation, and a per-target conditioning diagnostic vector. At the nine-CR Mongolia calibration field, all nine are recovered, with a mean horizontal error of 1.37 m and a mean 3D error of 2.01 m. At a CR-absent suburban site in Suncheon, Republic of Korea, 18 scenes yield 39,822 clusters, 26,659 of which pass the conditioning gate. Over 16 response-enriched poles retained by author adjudication, the difference to the nearest conditioning-filtered cluster averages 3.19 m horizontally and 1.45 m vertically relative to the surveyed pole base. Because part of that set was selected after inspecting preliminary responses, these are conditional nearest-cluster agreements, not verified target accuracies, and no detection probability is claimed. In an exploratory comparison on the same set, contrast ranking retains more evaluation targets than power ranking at equal budgets, 16 against 9 of 16. Removing robust weighting and pruning degrades the mean Mongolia 3D error to 7.39 m; the DEM prior is non-critical, whereas the clustering radius is consequential in the dense pole field, with the count within 20 m falling from 16 to 9 as the radius grows from 15 m to 30 m. In a leave-scene-out check, 85.8% of 254 evaluable clusters persist in a scene excluded from their solutions, against 35.2% of 270 evaluable random positions: a complete-case enrichment of 78%, or 53–82% under cross-arm worst-case imputation of the unevaluable targets; neither estimate measures catalogue precision. Limited KOMPSAT-5 stacks can therefore yield few-metre nearest-cluster agreement at selected pole locations where stable multi-scene responses form.

The Korea Aerospace Research Institute is conducting continuous quality management to provide reliable Kompsat-5 SAR image products to users. In this paper, the Kompsat-5 satellite operation, data processing, quality management, and data provision were described. The operation and image mode characteristics of the Kompsat-5 satellite from the image point of view were described, and the classification and characteristics of image products provided to users were explained. In addition, image data acquisition, quality index measurement, and its results are described for quality management of SAR images. Finally, it explains how to search for and order Kompsat image product through the ARIRANG system to quickly provide users with image products whose quality has been confirmed through quality management. Kompsat product can be searched and ordered from the ARIRANG Satellite Search and Order System (https://ksatdb.kari.re.kr/arirang/).

Multitemporal synthetic aperture radar (SAR) images have been widely used for change detection and monitoring of the environment owing to their competency under all weather conditions. However, owing to speckle backgrounds and strong reflections, change detection in urban areas is challenging. In this study, to automatically extract changed objects, we developed a model that integrated change detection and object extraction in multiple Korean Multi-Purpose Satellite-5 (KOMPSAT-5) images. Initially, two arbitrary L1A-level SAR images were input into the proposed model, and after pre-processing, such as radio calibration and coordinate system processing, change detection was performed. Subsequently, the desired targets were automatically extracted from the change detection results. Finally, the model obtained images of the extraction targets and metadata, such as date and location. Noise was removed by applying scale-adaptive modification to the generated difference image during the change detection process, and the detection accuracy was improved by emphasizing the occurrence of the change. After polygonizing the pixel groups of the change detection map in the target extraction process, the morphology-based object filtering technique was applied to minimize the false detection rate. As a result of the proposed approach, the changed objects in the KOMPSAT-5 images were automatically extracted with 90% accuracy.

Flattening gamma naught was calculated using Korea Multipurpose Satellite 5 (KOMPSAT-5, K5) by correcting the radiometric distortion caused by geometric distortion over Ilam, Iran. The flattening gamma naught is not only the SAR core observation of Analysis Ready Data (ARD), which is utilized for artificial intelligence and big data, but also the basis for all fields of application that use the SAR brightness by providing the backscattering values only from surface characteristics. The flattening gamma naught data is provided with the same resolution as that of the K5 SAR image, so the data over the Ilam, Iran have the spatial resolution of the K5 Wide Swath mode of 20 m. Shuttle Radar Topography Mission (SRTM) DEM with a resolution of 30 m was oversampled to generate the flattening gamma naught, and shadow areas where flattening gamma naught generation was not possible were identified using GIM layer information provided with the K5 image. In order to determine the reliability of the calculated flattening gamma naught, histogram analysis and tendency according to the incident angle were investigated, and the performance was verified by comparing it with other backscattering coefficients. Details of the algorithm and procedure are presented in previous studies and reference papers.

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)