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Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters as well as analyzing the sharpness of they themselves measured through image analysis. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW) and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.

As the use of unmanned aerial vehicle (UAV) images rapidly increases so does the need for precise radiometric calibration. For UAV images, relative radiometric calibration is required in addition to the traditional vicarious radiometric calibration due to the small field of view. For relative radiometric calibration, some UAVs install irradiance sensors, but most do not. For UAVs without them, an intelligent scheme for relative radiometric calibration must be applied. In this study, a relative radiometric calibration method is proposed to improve the quality of a reflectance map without irradiance measurements. The proposed method, termed relative calibration by the optimal path (RCOP), uses tie points acquired during geometric calibration to define the optimal paths. A calibrated image from RCOP was compared to validation data calibrated with irradiance measurements. As a result, the RCOP method produces seamless mosaicked images with uniform brightness and reflectance patterns. Therefore, the proposed method can be used as a precise relative radiometric calibration method for UAV images.

Current precise geometric correction of Geostationary Ocean Color Imager (GOCI) image slots is performed by shoreline matching. However, it is troublesome to handle slots with few or no shorelines, or slots covered by clouds. Geometric correction by frequency matching has been proposed to handle these slots. In this paper, we further extend previous research on frequency matching by comparing the performance of three frequency domain matching methods: phase correlation, gradient correlation, and orientation correlation. We compared the performance of each matching technique in terms of match success rate and geometric accuracy. We concluded that the three frequency domain matching method with peak search range limits was comparable to geometric correction performance with shoreline matching. The proposed method handles translation only, and assumes that rotation has been corrected. We need to do further work on how to handle rotation by frequency matching.

This study investigates the geometric and spatial image quality analysis of KOMPSAT-3A stereo pair. KOMPSAT-3A is, the latest satellite of KOMPSAT family, a Korean earth observation satellite operating in optical bands. A KOMPSAT-3A stereo pair was taken on 23 November, 2015 with 0.55 m ground sampling distance over Terrassa area of Spain. The convergence angle of KOMPSAT-3A stereo pair was estimated as 58.68˚. The investigation was assessed through the evaluation of the geopositioning analysis, image quality estimation and the accuracy of automatic Digital Surface Model (DSM) generation and compared with those of KOMPSAT-3 stereo pair with the convergence angle of 44.80˚ over the same area. First, geopositioning accuracy was tested with initial rational polynomial coefficients (RPCs) and after compensating the biases of the initial RPCs by manually collected ground control points. Then, regarding image quality, relative edge response was estimated for manually selected points visible from two stereo pairs. Both of the initial and biascompensated positioning accuracy and the quality assessment result expressed that KOMPSAT-3A images showed higher performance than those of KOMPSAT-3 images. Finally, the accuracy of DSMs generated from KOMPSAT-3A and KOMPSAT-3 stereo pairs were examined with respect to the reference LiDAR-derived DSM. The various DSMs were generated over the whole coverage of individual stereo pairs with different grid spacing and over three types of terrain; flat, mountainous and urban area. Root mean square errors of DSM from KOMPSAT-3A pair were larger than those for KOMPSAT-3. This seems due to larger convergence angle of the KOMPSAT-3A stereo pair.

KOMPSAT-3 is an optical high-resolution earth observation satellite launched on 19 May 2012. It provides panchromatic optical images at a nominal ground sampling distance (GSD) of 70 cm. This letter provides geometric accuracy evaluation of KOMPSAT-3 stereo images, including position determination and automated digital surface model (DSM) generation. Bias-compensated KOMPSAT-3 stereo achieved positioning accuracy of 0.65 m and 0.70 m in horizontal and vertical directions, respectively. Bias-compensated KOMPSAT-3 images were utilized for automated DSM generation. A DSM showed height errors of about 3.8 m (RMS) when all grids were compared with reference LIDAR data. Geometric performance of KOMPSAT-3 images was also compared with that of WorldView-1 images whose nominal GSD is 46 cm, over the same test area. The positioning accuracy and the accuracy of DSM generation from KOMPSAT-3 were slightly inferior to those of WorldView-1. However, overall performance of the two satellite images was comparable...

Techniques for measuring the position and orientation of an object from corresponding images are based on the principles of epipolar geometry in the computer vision and photogrammetric fields. Contributing to their importance, many different approaches have been developed in computer vision, increasing the automation of the pure photogrammetric processes. The aim of this paper is to evaluate the main differences between photogrammetric and computer vision approaches for the pose estimation of an object from image sequences, and how these have to be considered in the choice of processing technique when using a single camera. The use of a single camera in consumer electronics has enormously increased, even though most 3D user interfaces require additional devices to sense 3D motion for their input. In this regard, using a monocular camera to determine 3D motion is unique. However, we argue that relative pose estimations from monocular image sequences have not been studied thoroughly by comparing both photogrammetry and computer vision methods. To estimate motion parameters characterized by 3D rotation and 3D translations, estimation methods developed in the computer vision and photogrammetric fields are implemented. This paper describes a mathematical motion model for the proposed approaches, by differentiating their geometric properties and estimations of the motion parameters. A precision analysis is conducted to investigate the main characteristics of the methods in both fields. The results of the comparison indicate the differences between the estimations in both fields, in terms of accuracy and the test dataset. We show that homography-based approaches are more accurate than essential-matrix or relative orientation&ndash, based approaches under noisy conditions.

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.

Geometric correction is fundamental in producing high quality satellite data products. However, the geometric correction for ocean color sensors, e.g., Geostationary Ocean Color Imager (GOCI), is challenging because the traditional method based on ground control points (GCPs) cannot be applied when the shoreline is absent. In this study, we develop a hybrid geometric correction method, which applies shoreline matching and frequency matching on slots with shorelines and without shorelines, respectively. Frequency matching has been proposed to estimate the relative orientation between GOCI slots without a shoreline. In this paper, we extend our earlier research for absolute orientation and geometric correction by combining frequency matching results with shoreline matching ones. The proposed method consists of four parts: Initial sensor modeling of slots without shorelines, precise sensor modeling through shoreline matching, relative orientation modeling by frequency matching, and generation of geometric correction results using a combination of the two matching procedures. Initial sensor modeling uses the sensor model equation for GOCI and metadata in order to remove geometric distortion due to the Earth&rsquo, s rotation and curvature in the slots without shorelines. Precise sensor modeling is performed with shoreline matching and random sample consensus (RANSAC) in the slots with shorelines. Frequency matching computes position shifts for slots without shorelines with respect to the precisely corrected slots with shorelines. GOCI Level 1B scenes are generated by combining the results from shoreline matching and frequency matching. We analyzed the accuracy of shoreline matching alone against that of the combination of shoreline matching and frequency matching. Both methods yielded a similar accuracy of 1.2 km, which supports the idea that frequency matching can replace traditional shoreline matching for slots without visible shorelines.

3D tracking plays a vital role in 3D applications by enhancing interaction between real and virtual world. We present various real-time 3D motion estimation approaches developed in photogrammetry and computer vision fields and compare their performance. The methods developed in both fields estimates 3D motion of a moving object relative to a camera or equivalently moving camera relative to the object in an image sequence when its corresponding features are known at different times. We reviewed 3D motion models formulated by different methods related to their geometric properties. We implemented four different methods and analyzed their performance results. Comparison from test datasets from image sequences demonstrated that homography based approaches in both fields were more accurate than relative orientation or essential matrix based approaches under noisy situations.

This research investigated the positioning accuracy and imaging geometry of dual-sensor stereo pairs in comparison with those of conventional single-sensor stereo pairs. First, we discuss the difference between single- and dual-sensor stereo geometry and suggest that bisector elevation (BIE) and asymmetry angle, in addition to convergence angle, are important considerations for interpreting dual-sensor stereo geometry. We point out that for single-sensor BIE angles and asymmetry angles are close to ideal values, and their importance has not been highlighted. We show that dual-sensor stereo may produce small BIE angles and large asymmetry angles, and such angles may lead to weak geometry and accuracy degradation of dual-sensor stereo. Second, we compare rigorous sensor models (RSMs) with rational function models (RFMs) for handling dual-sensor stereo. For stable dual-sensor stereo, both models show similar performance. However, for dual-sensor stereo with weak geometry, RSMs produce better positioning accuracies than RFMs.

Minimization of visual fatigue is important for production of high quality stereoscopic 3D contents. Vertical disparity of stereo images occurred during contents production is considered as the main factor of visual fatigue. To ensure correct stereoscopy vertical disparity needs to be eliminated. In this paper, a method for automated image alignment was proposed for Stereoscopic 3D contents generation and post-processing steps. The proposed method consists of two parts: rectification for image alignment and camera motion detection. The proposed method showed that its rectification performance was the most superior among the existing methods tested and that camera motion detection had a success rate of 98.35%. Through these evaluations, we confirmed that the proposed method can be effectively applied to 3D contents production.

This paper reports the geolocation accuracy of KOMPSAT-3 imagery. KOMPSAT-3 was launched successfully on May 18, 2012 and has been released last March. In this paper, we have checked the geolocation accuracy of initial sensor model, precise sensor model and stereo-and multi-image model using four KOMPSAT-3 images covering the same area. The KOMPSAT-3 images without GCPs provided the geolocation accuracy of about 30m and the geocorrected KOMPSAT-3 images provided the geolocation accuracy of about 1m or less. KOMPSAT-3 stereo- and multi-images models yield three- dimensional points with sub-meter accuracy in horizontal and vertical direction. Overall, KOMPSAT-3 showed much improved performance in terms of the geolocation accuracy over KOMPSAT-2. KOMPSAT-3 is expected to be able to replace foreign satellite data with sub-meter accuracy level for achieving accurate geometric information.

In this paper, a method of using high resolution stereo images is proposed to efficiently detect DSM errors. Automatically generated DSMs from stereo matching can be a useful solution to acquire DSM data in various aspects but they may include many gross errors coming from automatic processing. Therefore, a method to detect the gross errors on DSM is required for efficient DSM update. In this paper, stereo analysis using high resolution stereo images was investigated to represent reliability of DSM grids. The analysis enabled automatic detection of the gross errors which greatly influenced DSM quality. We used the reference DSM to assess reliability of our proposed method. We confirmed from experimental results that our method can be a valuable DSM errors analysis for efficient DSM correction. Our method is useful to analyze and improve DSM accuracy for various types of DSM and DEM. It is expected that our approach can be exploited for achievement of reliable DSM and DEM.

This article presents a new method for semi-automatic extraction of building information (height, shape, and footprint location) from monoscopic urban scenes. The proposed method is to expand Semi-automatic Volumetric Shadow Analysis (SVSA), which can handle occluded building footprints or shadows semi-automatically. SVSA can extract wrong building information from a single high resolution satellite image because SVSA is influenced by extracted shadow area, image noise and objects around a building. The proposed method can reduce the disadvantage of SVSA by using multi-spectral images. The proposed method applies SVSA to panchromatic and multi-spectral images. Results of SVSA are used as parameters of a cost function. A building height with maximum value of the cost function is determined as actual building height. For performance evaluation, building heights extracted by SVSA and the proposed method from Kompsat-2 images were compared with reference heights extracted from stereo IKONOS. The result of performance evaluation shows the proposed method is a more accurate and stable method than SVSA.

This article presents a new approach to automatic extraction of building heights from monoscopic urban scenes. A volumetric shadow analysis VSA method was proposed previously for extraction of 3D building information height, shape, and footprint location and for handling occluded building footprints or shadows. It determined building heights by adjusting building height manually until the projected shadows generated for an assumed height and actual shadows in the image matched. In this article, we propose an intelligent scheme based on the VSA for automatic building height extraction. We achieve this by checking the location change of projected shadow lines with respect to the actual shadow regions while building heights are increased incrementally. In this article, the performance of the proposed automatic height extraction was compared to that of manual extraction. The method was first applied to IKONOS, KOMPSAT-2, QuickBird, and Worldview-1 images with manually extracted building roofs. The root mean square error RMSE of building heights was under 3 m by automatic height extraction and 2 m by manual extraction. The RMSE of building footprint location was close to twice that of image ground sample distance GSD by automatic height extraction and under twice that of image GSD by manual extraction. These results support the capability of the proposed method in automatic height extraction from a single image efficiently and accurately, and in handling occluded building footprints and shadows. Second, the method was combined with an existing roof extraction method and tested for automated building roof extraction. The results showed that the proposed method can also provide a powerful cue for automatic building roof extraction from a single image.

Epipolar resampling is the procedure of eliminating vertical disparity between stereo images. Due to its importance, many methods have been developed in the computer vision and photogrammetry field. However, we argue that epipolar resampling of image sequences, instead of a single pair, has not been studied thoroughly. In this paper, we compare epipolar resampling methods developed in both fields for handling image sequences. Firstly we briefly review the uncalibrated and calibrated epipolar resampling methods developed in computer vision and photogrammetric epipolar resampling methods. While it is well known that epipolar resampling methods developed in computer vision and in photogrammetry are mathematically identical, we also point out differences in parameter estimation between them. Secondly, we tested representative resampling methods in both fields and performed an analysis. We showed that for epipolar resampling of a single image pair all uncalibrated and photogrammetric methods tested could be used. More importantly, we also showed that, for image sequences, all methods tested, except the photogrammetric Bayesian method, showed significant variations in epipolar resampling performance. Our results indicate that the Bayesian method is favorable for epipolar resampling of image sequences.

In this paper, we introduce a newly developed photogrammetric software for automatic generation of high quality geo-products and its performance assessment carried out using various satellite images. Our newly developed software provides the latest techniques of an optimized sensor modelling, ortho-image generation and automated Digital Elevation Model (DEM) generation for diverse remote sensing images. In particular, images from dual- and multi-sensor images can be integrated for 3D mapping. This can be a novel innovation toward a wider applicability of remote sensing data, since 3D mapping has been limited within only single-sensor so far. We used Kompsat-2, Ikonos, QuickBird, Spot-5 high resolution satellite images to test an accuracy of 3D points and ortho-image generated by the software. Outputs were assessed by comparing reliable reference data. From various sensor combinations 3D mapping were implemented and their accuracy was evaluated using independent check points. Model accuracy of 1~2 pixels or better was achieved regardless of sensor combination type. The high resolution ortho-image results are consistent with the reference map on a scale of 1:5,000 after being rectified by the software and an accuracy of 1~2 pixels could be achieved through quantitative assessment. The developed software offers efficient critical geo-processing modules of various remote sensing images and it is expected that the software can be widely used to meet the demand on the high-quality geo products.

DEM matching is a technique to match two surfaces or two DEMs, at different reference frames. It was originally proposed to replace the need of ground control points for absolute orientation of perspective images. This paper examines DEM matching for precise mapping of pushbroom images without ground control points. We proved that DEM matching based on 3D similarity transformation can be used when model errors are only on the platform’s position and attitude biases. We also proposed how to estimate bias errors and how to update rigorous pushbroom sensor models from DEM matching results. We used a SPOT-5 stereo pair at ground sampling distance of 2.5 m and a reference DEM dataset at grid spacing of 30 m and showed that rigorous pushbroom models with accuracy better than twice of the ground sampling distance both in image and object space have been achieved through DEM matching. We showed further that DEM matching based on 3D similarity transformation may not work for pushbroom images with drift or drift rate errors. We discussed the effects of DEM outliers on DEM matching and automated removal of outliers. The major contribution of this paper is that we validate DEM matching, theoretically and experimentally, for estimating position and attitude biases and for establishing rigorous sensor models for pushbroom images.

Accurate DEM(Digital Elevation Model) generation using satellite images is an active research topic. This paper focuses on generation of a DEM with multiple Kompsat-2 images. For DEM generation, we applied an orbit-attitude sensor model and a RPM sensor model to stereo and multiple Kompsat-2 images respectively. For matching, we used an object-space based matching method. Through the result of this experiment, we could confirm that the sensor model from multiple images is more accurate than the model from stereo images. Also DEM from multiple images gave much better performance than DEM from stereo images.

A digital surface model (DSM) is an important geospatial infrastructure used in various fields. In this paper, we deal with how to improve the quality of DSMs generated from stereo image matching. During stereo image matching, there are outliers due to mismatches, and non-matching regions due to match failure. Such outliers and non-matching regions have to be corrected accurately and efficiently for high-quality DSM generation. This process has been performed by applying a local distribution model, such as inverse distance weight (IDW), or by forming a triangulated irregular network (TIN). However, if the area of non-matching regions is large, it is not trivial to interpolate elevation values using neighboring cells. In this study, we proposed a new DSM interpolation method using a 3D mesh model, which is more robust to outliers and large holes. We compared mesh-based DSM with IDW-based DSM and analyzed the characteristics of each. The accuracy of the mesh-based DSM was a 2.80 m root mean square error (RMSE), while that for the IDW-based DSM was 3.22 m. While the mesh-based DSM successfully removed empty grid cells and outliers, the IDW-based DSM had sharper object boundaries. Because of the nature of surface reconstruction, object boundaries appeared smoother on the mesh-based DSM. We further propose a method of integrating the two DSMs. The integrated DSM maintains the sharpness of object boundaries without significant accuracy degradation. The contribution of this paper is the use of 3D mesh models (which have mainly been used for 3D visualization) for efficient removal of outliers and non-matching regions without a priori knowledge of surface types.

This paper addresses the problem of generating digital elevation models from satellite images taken by linear pushbroom cameras. Since there exist unique geometric properties for linear pushbroom images, we argue that the conventional DEM generation schemes developed for perspective images are not suitable for satellite images. Using the geometric properties of linear pushbroom images, we design a new matching strategy optimized for linear pushbroom image in three aspects: conjugate search method, correlation patch design and match sequence determination. We will discuss in what aspect conventional approaches and our new approach differ and show how performance has improved by hiring proper techniques. A series of experiments using SPOT panchromatic stereo pairs showed that our approach outperformed conventional approaches in terms of accuracy and processing time.

This paper investigates the positioning accuracy of image pairs achieved by integrating images from multiple satellites. High-resolution satellite images from IKONOS, QuickBird, and KOMPSAT-2 for Daejeon, Korea were combined to produce pairs of stereo images. From single-satellite stereo pairs to multiple-satellite image pairs, all available combinations were analyzed via a rational function model (RFM). The positioning accuracy of multiple-satellite pairs was compared to a typical single-satellite stereo pair. The results show that dual-satellite integration can be an effective alternative to single-satellite stereo imagery for horizontal position mapping, but is less accurate for vertical mapping. The integration of additional higher-resolution images can improve the overall accuracy of the existing two images, but, conversely, may result in lower accuracy when very weak convergence or bisector elevation (BIE) angles occur. This highlights that the use of higher resolution images may not ensure improved accuracy, as it can result in very weak geometry. The findings confirm that multiple-satellite images can replace or enhance typical stereo pairs, but also suggest the need for careful verification, including consideration of various geometric elements and image resolution. This paper reveals the potential, limitations, and important considerations for mapping applications using images from multiple satellites.

This paper describes the development of an approach for automatic building detection from aerial imagery. The development has concentrated on two aspects: to systematize the approach and to develop robust algorithms. The whole process of building detection is divided into four small stages of line extraction, line-relation-graph generation, building hypothesis generation, and building hypothesis verification. Robustness has been achieved by considering only the mathematical and geometric relations between lines in the course of generating building hypotheses. By preventing any assumptions related to intensity values of an image, this approach can be applied to more wider range of imagery. Graph structure was used for building hypothesis generation and it was shown that the use of graph structure could simplify the problem of building detection in a great deal. This paper reports several examples of building detection with the approach developed. The performance of each example is evaluated quantitatively with six quality measures defined here. The results of performance assessment support the robustness of the approach developed.

This paper presents a new approach to stable automatic extraction of building information from a high resolution satellite image set, panchromatic (PAN) image and multispectral (MS) images shot in a same date. The proposed approach is based on semi-automatic volumetric shadow analysis (SVSA). The proposed approach extracts a height and some information of a same building by SVSA from PAN image and MS images, respectively. The height and some information are used to calculate a maximum cost value by cost functions. A height with maximum cost value is determined as a true height. The true height is used to find a footprint position of the building. This paper also presents an automatic building extraction result by the proposed approach and roof extraction. For test, a Kompsat-2 image set was used. The results showed that the proposed approach can extract building information stably from a high resolution satellite image set.

This paper proposes a new algorithm for correcting camera misalignments and removing Y-parallax between stereo images. Traditionally, this task has been tackled by estimating the fundamental matrix directly from tie points and applying epipolar resampling based on the fundamental matrix. We propose a photogrammetric approach for the task: Instead of direct estimation of the fundamental matrix, we estimate relative orientation between stereo images, calculating the fundamental matrix from the relative orientation and applying epipolar resampling. We compared the performance of the proposed algorithm with the traditional one. We found that the proposed one could generate epipolar resampled images with less distortions and could be more effective for 3D image generation.

The expanding IPTV (Internet Protocol Television) market has begun to include the next generation IPTV services for new user experiences. And Open IPTV is one of the very important next services for the subscribers who want the chance to experience more various contents and services easily. This paper presents a policy-based service overlay IPTV framework for open strategy. We suggest that subscribers should be able to choose an EPG (Electronic Program Guide) from multiple EPGs in Open IPTV, and the EPG apply a personal policy in the selection problem of overlapped contents. We design a framework for the requirements, and the framework is based on the ITU-T IPTV NGN (Next Generation Network) framework with non-IMS (IP Multimedia Subsystem).

The current practice in photogrammetry adopts scene- based approach for establishing sensor models of satellite images. One prepares ground control points (GCPs) for a scene of interest, applies bundle adjustments and estimates orientation parameters for the scene. In this paper, we investigate rigorous sensor models for modeling satellite orbits, instead of individual scenes, through bundle adjustment process. Using GCPs from one scene we aim to estimate orientation parameters that can be applied to all images acquired from the same orbit. We selected two types of well-known rigorous sensor models: collinearity- based type and orbit-based type. For each type, we defined seven adjustment parameter sets and tested the performance of orbit modeling for each the sensor model type and adjustment parameter set. We used two strips with a length of 420 km (seven SPOT-3 scenes), which constituted a stereo pair. With each strip, we tested the feasibility of estimating orientation parameters for the whole image strip using GCPs from single scene. We also tested the feasibility of absolute orientation from orbit modeling, i.e. the ability of acquiring 3D information from a stereo strip using GCPs from single scene. Results showed that although there was little difference between the two models for individual scene modeling, for orbit modeling the orbit-based model showed better results. The orbit-based model that used attitude biases as adjustment parameters produced promising performances for modeling the orbital segments and for absolute orientation.

Extraction of man-made structures from satellite images is one of the essential issues in remote sensing and many techniques were proposed for building extraction from high resolution satellite images. However, most of them rely stereo analysis or additional data sources such as LIDAR for retrieval of 3D building information. This paper proposes a semiautomatic approach that extracts 3D building information from single satellite images. In this approach, after measuring boundaries of a building roof we determine the building height by projecting building shadow onto the image space until the projected shadow matches the actual shadow. Then we extract building footprints by translating roof boundaries along the direction of vertical edges to the amount determined by the building height. To increase the level of automation, we devised a technique that adjusts the height so that the projected shadow matches the actual shadow and determine building height automatically. We also devised a technique to extract roof boundaries by clicking only two corner points. A panchromatic IKONOS image and Quickbird image over Deajeon area were used to test proposed algorithms and a IKONO stereo pair was used to get reference building heights. While building heights determined manually showed the accuracy of an RMS error of 1.66 m, the heights determined automatically showed an RMS error of 1.86 m. The results show that 3D building information can be extracted from single satellite images and that the automation approaches proposed in this paper worked.

Precision correction is the process of geometrically aligning images to a reference coordinate system using GCPs (Ground Control Points). Many application of remote sensing data, such as change detection, mapping, and environmental monitoring, rely on the accuracy of precision correction. However it is very time consuming, laborious and expensive process. In this research, we propose a new method for automatic precision correction of satellite images using the GCP chips collected from lower resolution satellite images to reduce or eliminate the number of GCPs required. Different Earth observation satellites provide the satellite images of different resolution, different swath and different position accuracy. In general, the satellite images of lower resolution have wider swath and vice versa. In that sense, if we can utilize the GCP chips collected from lower resolution satellite images to do precision correction of the higher resolution satellite images then the number of GCPs required to have a precisely corrected images of the same area would be reduced. In this experiment, we used GCP chips collected from Landsat-7 panchromatic images of 15 m resolutions to perform precision correction of the KOMPSAT-1 EOC images of 6.7 m resolutions. In this case, since Landsat-7 images provide higher positional accuracy of less than 250 m at the systematically corrected level (L1G) comparing about 2 km positional accuracy of KOMPSAT-1, GCPs collected from level 1G of Landsat-7 images can provide enough accuracy in certain applications. To utilize this approach easily, this study would be applied to the automatic precision correction method developed previously, which exploits the normalized cross correlation and the RANSAC (Random Sample Consensus) algorithm to removed the outliers of matching results. The initial result showed the possibility of using GCP chips of lower resolution to carry out precision correction in the frame of the automatic precision correction method utilizing RANSAC algorithm.

Describes an approach to reduce the execution time of an area-based stereo matching technique by defining a patch (or window) carefully. It is shown that the conventional way of defining a patch contains redundancy and that a patch defined otherwise improves the performance in terms of coverage and speed while accuracy degradation is nominal. This paper also describes an attempt of defining optimal patch for stereo matching.

KOMPSAT-2 (Korea Multi-Purpose Satellite) is a new high resolution Earth observation satellite to be launched in 2004 by the Korean Government. This satellite is capable of acquiring 1m panchromatic and 4m multispectral images and will transmit the image to ground at 320 Mbps. This paper will address the work being carried out for the development of image reception, processing and distribution system for the KOMPSAT-2 image data. The development work has been focused on automation, speed, reliability, integrity, cost, expandability and security of the system. The system was divided into three sub-systems and three sub-systems into 20 software modules. An emphasis was put to the integration of value-added processing modules into the whole system. With this, we intended to fully automate the processing chain from image reception to the generation of value-added information. The development work is still undergoing and this paper will report the design of the system, progress and lessons learned.

Over the last few years, there has been increasing interest, principally from the telecommunications and airline industries in the supply of worldwide high resolution databases of urban areas. High resolution (l–5m) spaceborne data is beginning to address these requirements. Critical to the successful exploitation of these spaceborne data is the development of algorithms for automated or the semi-automated extraction of building objects using cartographic data and the automated extraction of DEMs.

For automatic building extraction, the need of the fusion of several image understanding cues has been emphasised. In this paper, the fusion of a stereoscopic (pyramidal matching) and a monoscopic technique (building detection) was investigated. In the fusion technique proposed, building detection output is used as boundaries of height interpolation and pyramidal matching output as the source of height information. After height interpolation, buildings are reconstructed in 3D space. This fusion technique was tested with several examples and proved to be very powerful. The results of this paper support the role of fusion in more complete image understanding.

The main objective of this research is automatic generation of more accurate DEM/ortho-image and, by extension, to update existing DEMs and ortho-images database automatically by using high resolution satellite images. In the first part, we tested the possibility of automatic GCP and tie-point generation for DEMs and ortho-images generation. We extract Ground Control Points (GCPs) automatically from high resolution satellite image by using existing ortho-images and existing DEMs. In second part, we tested whether high resolution images can be used to update existing DEMs and ortho-images. For this, we performed automatic generation of DEMs/ortho-images by using true GCPs obtained by GPS surveying. For testing the method proposed, we used IKONOS, SPOT 5, QuickBird, Kompsat-2 high resolution satellite images. Assessment for results supports that automatic generation of DEMs/ortho-images was performed successfully. We generated a DEM from IKONOS images fully automatically by using GCPs generated from existing DEMs and ortho-images. Newly updated DEM was compared with existing data. Updated DEM shows more detailed surface, and the result of validation shows that the accuracy is similar with that of existing DEMs/ortho-images.