• Korean Journal of Remote Sensing
• /
• v.39 no.1
• /
• pp.111-127
• /
• 2023

In visual slam and 3D data modeling, the Iterative Closest Point method is a primary fundamental algorithm, and many technical fields have used this method. However, it relies on search methods that take a high search time. This paper solves this problem by applying an effective point cloud refinement method. And this paper also accelerates the point cloud registration process with an indexing scheme using the spatial decomposition method. Through some experiments, the results of this paper show that the proposed point cloud refinement method helped to produce better performance.

• 한국공간정보시스템학회:학술대회논문집
• /
• 2005.05a
• /
• pp.345-349
• /
• 2005

Massive amount of satellite image processing such asglobal/continental-level analysis and monitoring requires automated and speedy georegistration. There could be two major automated approaches: (1) rigid mathematical modeling using sensor model and ephemeris data; (2) heuristic co-registration approach with respect to existing reference image. In case of ETM+, the accuracy of the first approach is known as RMSE 250m, which is far below requested accuracy level for most of satellite image processing. On the other hands, the second approach is to find identical points between new image and reference image and use heuristic regression model for registration. The latter shows better accuracy but has problems with expensive computation. To improve efficiency of the coregistration approach, the author proposed a pre-qualified matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with correlation coefficient. Throughout the pre-qualification approach, the computation time was significantly improved and make the registration accuracy is improved. A prototype was implemented and tested with the proposed algorithm. The performance test of 14 TM/ETM+ images in the U.S. showed: (1) average RMSE error of the approach was 0.47 dependent upon terrain and features; (2) the number average matching points were over 15,000; (3) the time complexity was 12 min per image with 3.2GHz Intel Pentium 4 and 1G Ram.

• Korean Journal of Remote Sensing
• /
• v.26 no.3
• /
• pp.325-333
• /
• 2010

Image registration involves overlapping images of an identical region and assigning the data into one coordinate system. Image registration has proved important in remote sensing, enabling registered satellite imagery to be used in various applications such as image fusion, change detection and the generation of digital maps. The image descriptor, which extracts matching points from each image, is necessary for automatic registration of remotely sensed data. Using contrast enhancement algorithms such as histogram equalization and image stretching, the normalized data are applied to the image descriptor. Drawing on the different spectral characteristics of high resolution satellite imagery based on sensor type and acquisition date, the applied normalization method can be used to change the results of matching interest point descriptors. In this paper, the matching points by scale invariant feature transformation (SIFT) are extracted using various contrast enhancement algorithms and injection of Gaussian noise. The results of the extracted matching points are compared with the number of correct matching points and matching rates for each point.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.27 no.5
• /
• pp.585-597
• /
• 2009

This research aims to develop a registration method to remove the geometric inconsistency between aerial images and LIDAR data acquired from an airborne multi-sensor system. The proposed method mainly includes registration primitives extraction, correspondence establishment, and EOP(Exterior Orientation Parameters) adjustment. As the registration primitives, we extracts planar patches and intersection edges from the LIDAR data and object points and linking edges from the aerial images. The extracted primitives are then categorized into horizontal and vertical ones; and their correspondences are established. These correspondent pairs are incorporated as stochastic constraints into the bundle block adjustment, which finally precisely adjusts the exterior orientation parameters of the images. According to the experimental results from the application of the proposed method to real data, we found that the attitude parameters of EOPs were meaningfully adjusted and the geometric inconsistency of the primitives used for the adjustment is reduced from 2 m to 2 cm before and after the registration. Hence, the results of this research can contribute to data fusion for the high quality 3D spatial information.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.15 no.5
• /
• pp.676-686
• /
• 2012

This paper presents a sensor bias estimation method with serial fusion for asynchronous multisensory systems. Serial fusion processes the sensor measurements in a first-come-first-serve basis and it plays an essential role in asynchronous fusion in practice. The proposed algorithm generates the bias measurements using fusion estimates and sensor measurements for bias estimation, and compensates the sensor biases in fusion tracks. A simulation study indicates that the proposed algorithm has the superior performance in bias estimation and accurate tracking.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_3
• /
• pp.1351-1367
• /
• 2018

Image registration is an essential process for image fusion, change detection and time series analysis using multi-sensor images. For this purpose, we need to detect accurately the difference of scale and rotation between the multi-sensor images with difference spatial resolution. In this paper, we propose a new feature matching method using variable circular template for image registration between multi-resolution images. The proposed method creates a circular template at the center of a feature point in a coarse scale image and also a variable circular template in a fine scale image, respectively. After changing the scale of the variable circular template, we rotate the variable circular template by each predefined angle and compute the mutual information between the two circular templates and then find the scale, the angle of rotation and the center location of the variable circular template, respectively, in fine scale image when the mutual information between the two circular templates is maximum. The proposed method was tested using Kompsat-2, Kompsat-3 and Kompsat-3A images with different spatial resolution. The experimental results showed that the error of scale factor, the error of rotation angle and the localization error of the control point were less than 0.004, $0.3^{\circ}$ and one pixel, respectively.

• ETRI Journal
• /
• v.29 no.4
• /
• pp.411-420
• /
• 2007

The steady evolution of mapping technology is leading to an increasing availability of multi-sensory geo-spatial datasets, such as data acquired by single-head frame cameras, multi-head frame cameras, line cameras, and light detection and ranging systems, at a reasonable cost. The complementary nature of the data collected by these systems makes their integration to obtain a complete description of the object space. However, such integration is only possible after accurate co-registration of the collected data to a common reference frame. The registration can be carried out reliably through a triangulation procedure which considers the characteristics of the involved data. This paper introduces algorithms for a multi-primitive and multi-sensory triangulation environment, which is geared towards taking advantage of the complementary characteristics of spatial data available from the above mentioned sensors. The triangulation procedure ensures the alignment of involved data to a common reference frame. The devised methodologies are tested and proven efficient through experiments using real multi-sensory data.

• Korean Journal of Remote Sensing
• /
• v.27 no.2
• /
• pp.141-150
• /
• 2011

Precise image-to-image registration is required to process multi-sensor data together. The purpose of this paper is to develop an algorithm that register between high-resolution optical and SAR images using linear features. As a pre-processing step, initial alignment was fulfilled using manually selected tie points to remove any dislocations caused by scale difference, rotation, and translation of images. Canny edge operator was applied to both images to extract linear features. These features were used to design a cost function that finds matching points based on their similarity. Outliers having larger geometric differences than general matching points were eliminated. The remaining points were used to construct a new transformation model, which was combined the piecewise linear function with the global affine transformation, and applied to increase the accuracy of geometric correction.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.6
• /
• pp.667-675
• /
• 2011

In this paper, we propose an automatic image-to-image registration between high resolution multi-sensor images. To do this, TerraSAR-X image was shifted according to the initial translation differences of the x and y directions between images estimated using Mutual Information method. After that, the Canny edge operator was applied to both images to extract linear features. These features were used to design a cost function that finds matching points based on the similarities of their locations and gradient orientations. For extracting large number of evenly distributed matching points, only one point within each regular grid constructed throughout the image was extracted to the final matching point pair. The model, which combined the piecewise linear function with the global affine transformation, was applied to increase the accuracy of the geometric correction, and the proposed method showed RMSE lower than 5m in all study sites.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.766-769
• /
• 2005

In a ubiquitous environment made up of multiple sensors, most sensors participate in communications with limited battery, and the sensor node isn't able to participate in communications when all the battery is used up. When an existing authentication method is used for the sensor node which has to participate in a long term communication with limited battery, it creates a problem by making the length of network maintenance or sensor node's operation time relatively shorte. Therefore, a network structure where RM (Register Manager) node and AM (Authentication Manager) node are imported to solve the energy consumption problem during a communication process is presented in this thesis. This offers a low power protocol based on safety through a mutual authentication during communications. Through registration and authentication manager nodes, each sensor nodes are ensured of safety and the algorithm of key's generation, encryption/descramble and authentication is processed with faster operation speed. So the amount of electricity used up during the communications between sensor nodes has been evaluated. In case of the amount of electrical usage, an average of $34.783\%$ for the same subnet and 36.855 for communications with two different subnets, are reduced. The proposed method is a protocol which maintains the limited battery for a long time to increase the effectiveness of energy usage in sensor nodes and can also increase the participation rate of communication by sensor nodes.