One of the important image processing technologies is visual odometry (VO) technology. VO estimates platform motion through a sequence of images. VO is of interest in the virtual reality (VR) industry as well as the automobile industry because the construction cost is low. In this study, we developed stereo visual odometry (SVO) based on photogrammetric geometric interpretation. The proposed method performed feature optimization and pose estimation through photogrammetric bundle adjustment. After corresponding the point extraction step, the feature optimization was carried out with photogrammetry-based and vision-based optimization. Then, absolute orientation was performed for pose estimation through bundle adjustment. We used ten sequences provided by the Karlsruhe institute of technology and Toyota technological institute (KITTI) community. Through a two-step optimization process, we confirmed that the outliers, which were not removed by conventional outlier filters, were removed. We also were able to confirm the applicability of photogrammetric techniques to stereo visual odometry technology.

UAV remote sensing is suitable for urgent image monitoring and periodic observation of an area of interest. To observe a target area using UAVs, many images must be acquired because of the narrow image coverage of UAVs. To increase the efficiency of UAV remote sensing, UAV mosaicking is used to create a single image from multiple UAV images. In order to maintain the strength of rapid UAV deployment, UAV mosaicked images have to be quickly generated through image-based mosaicking techniques. In addition, it is necessary to improve the mosaic errors of image-based techniques that often occur in contrast to terrain-based techniques. Relief displacement is a major source of mosaic error and can be detected by utilizing a terrain model. We have proposed an image-based mosaicking technique utilizing TIN, which is a model that can represent terrain with discontinuously acquired height information of ground points. Although the TIN is less accurate than DSM, it is simpler and faster to utilize for image mosaicking. In our previous work, we demonstrated fast processing speed of mosaicking using TIN-based image tiepoints. In this study, we improve the quality of image-based mosaicking techniques by optimizing seamline-based TIN geometry. Three datasets containing buildings with large relief displacement were used in this study. The experiment results showed that the TIN based on the proposed method improved the mosaic error caused by relief displacement significantly.

To take full advantage of rapidly deployable unmanned aerial vehicles (UAVs), it is essential to effectively compose many UAV images into one observation image over a region of interest. In this paper, we propose fast image mosaicking using a triangulated irregular network (TIN) constructed from tiepoints. We conduct pairwise tiepoint extraction and rigorous bundle adjustment to generate rigorous tiepoints. We apply a bucketing algorithm to the tiepoints and generate evenly distributed tiepoints. We then construct a TIN from the bucketed tiepoints and extract seamlines for image stitching based on the TIN. Image mosaicking is completed by mapping UAV images along the seamlines onto a reference plane. The experimental results showed that the image mosaicking based on a TIN of bucketed tiepoints could produce image mosaics with stable and fast performance. We expect that our method could be used for rapid image mosaicking.

Jinyeong Kim, Sun-Hwa Kim, Jeong Eun, Sung-Joo Yoon, Han-Sol Ryu, Tae-Ho Kim. Korean J. Remote Sens. 2025;41:1077-90. https://doi.org/10.7780/kjrs.2025.41.6.11

The Normalized Difference Vegetation Index (NDVI) derived from polar-orbiting satellites is widely used for vegetation monitoring; however, its temporal continuity is often limited by cloud contamination and fixed revisit cycles. To address this limitation, this study investigates the feasibility of using geostationary satellite observations to enhance the spatial completeness of Sentinel-2 NDVI at its standard revisit intervals through cloud gap-filling applications. Geostationary Ocean Color Imager II (GOCI-II) data (250 m) was used as input, while Sentinel-2 Multispectral Instrument (MSI) NDVI (10 m) served as the reference dataset. To enable cross-sensor integration, a data-driven transformation framework was developed to convert GOCI-II NDVI into MSI-like NDVI while preserving dominant spatial variation patterns rather than pursuing strict pixel-level super-resolution. The transformed NDVI was assessed through spatial comparisons and statistical metrics, including correlation coefficient, mean absolute error, root mean square error (RMSE), normalized RMSE, and structural similarity index measure. Results show that geostationary-derived NDVI captures broad spatial organization and field-scale variability observed in MSI NDVI. Building on this cross-scale consistency, cloud gap-filling experiments demonstrate that temporally adjacent transformed NDVI scenes maintain consistent variation patterns, supporting their complementary use for compensating cloud-induced gaps. Although reduced contrast and magnitude-dependent biases remain, primarily due to the large spatial resolution difference and sub-pixel heterogeneity, an intermediate-resolution (80 m) sensitivity analysis indicates improved stability when the resolution gap is reduced. Overall, these findings highlight the practical potential of integrating geostationary and polar-orbiting observations to improve NDVI spatial continuity in cloud-prone regions.

For efficient UAV (Unmanned Aerial Vehicle) image monitoring, it is essential to mosaic multiple UAV images into one seamless image. In a mosaicked image, relief displacement of terrain is a major source of error. It is difficult to form seamlines that avoid all areas of relief displacements. A seamline determination method alone is limited in reducing the mismatch error on mosaicked image. In this study, we constructed a TIN (Triangulated Irregular Network) using tiepoints generated by rigorous bundle adjustments. We detected the regions where relief displacement occurred using the slope of TIN facets. It was found that the error of mosaicked image were mostly on TIN facets with high slopes. Our method generated a mosaicked image after elimination of error-prone region and showed that the distortions were effectively removed. This study showed that the proposed method could produce mosaicked images with stable quality using geometric clues of TIN. We expected that our method can be used for UAV image mosaicking robust to mismatching factors.