Deep neural network based automatic grounding line delineation in DInSAR interferograms

The grounding line is a subsurface geophysical feature that divides a grounded ice sheet and floating ice shelf. Knowledge of its precise location is required for estimating ice sheet mass balance, as ice discharged from the interior is typically calculated at the grounding line [1]. Grounding lines move back and forth as ice shelves bend and flex due to ocean tides. Identifying their migration patterns can provide insights into understanding ice sheet dynamics and overall ice sheet stability [2] and thereby improve the accuracy of numerical ice sheet models. The spatial and temporal resolution of past and current satellite missions has enabled regular, continent-wide observation of Antarctica and other isolated glaciers with floating ice tongues. In particular, the high sensitivity of Interferometric SAR measurements to ground deformation has resulted in its application to grounding line location (GLL) mapping [3]. Specifically, the deformation at the grounding zone resulting from tidal flexure of the ice shelf is isolated from ice motion and topography in Differential InSAR (DInSAR) interferograms, under the assumption of steady ice velocity within the chosen temporal baseline. The tidal deformation is visible as a dense fringe belt and its landward limit is manually digitised as the GLL. Apart from being labour and time intensive, manual delineations are also inconsistent due to varying interpretations of experts in identifying the landward fringe, especially in areas with poor coherence or intricate fringe patterns. The concept of automatic GLL delineation has recently gained attention and seen the development of several methodologies. [4] demonstrated a semi-automatic method that estimates the fringe frequency of wrapped phase in DInSAR interferograms. The grounding zone can be directly identified by computing the gradient of the estimated frequencies, thereby avoiding phase unwrapping. However, this approach requires an a priori grounding zone location. [5] developed a deep learning based automatic delineation pipeline in which the proposed DNN was trained on real and imaginary components of DInSAR phases from Sentinel-1 acquisitions. This study further investigated the feasibility of DNNs for mapping the interferometric grounding line. The proposed DNN, based on the architecture of the Holistically-Nested Edge Detection network [6], was trained in a supervised manner, using manual delineations from the GLL product developed within ESA’s Antarctic Ice Sheet climate change initiative (AIS cci) project [7] as ground truth (Fig. 1 (a)). The GLL product contains manual delineations on 478 DInSAR interferograms computed from Sentinel-1A/B, ERS-1/2 and TerraSAR-X images acquired during 1992 - 2021. The training feature stack consists of four interferogram-based features: real and imaginary components, interferometric phase and pseudo coherence (which is estimated by applying a boxcar filter to interferometric phase) derived from the corresponding DInSAR interferograms and five auxiliary features derived from several compiled datasets: TanDEM-X Polar DEM [8], horizontal and vertical components of ice velocity [9], tidal amplitude [10] and atmospheric pressure [11]. An automatic workflow that handles the preparation of the training feature stack, training and inference of the neural network and the post processing of network generated delineations was developed. The performance of the neural network was evaluated as the median deviation of the network generated GLLs from the manual delineations, quantified using the PoLiS metric [12]. Additionally, the importance of individual features was indirectly gauged by training several networks with different feature subsets and comparing their median deviations from the ground truth. The DNN generated GLLs follow the landward limit of ice sheet flexure reasonably well, with the best network variant achieving a median deviation of 209 m from manual delineations.The contribution of auxiliary features was shown to be very weak, with their inclusion in the feature stack only slightly improving the delineation capability of the network. This finding is advantageous in terms of saving time, computational effort and memory in creating and storing the feature stack. References [1] E. Rignot and H. Thomas, “Mass balance of polar ice sheets,” Science, vol. 297, no. 5586, pp. 1502–1506, 2002. DOI: 10 . 1126 / science . 1073888. eprint: https : / / www . science . org / doi / pdf / 10 . 1126 / science.1073888. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.1073888. [2] C. Schoof, “Ice sheet grounding line dynamics: Steady states, stability, and hysteresis,” Journal of Geophysical Research: Earth Surface, vol. 112, no. F3, 2007. [3] E. Rignot, “Tidal motion, ice velocity and melt rate of petermann gletscher, greenland, measured from radar interferometry,” Journal of Glaciology, vol. 42, no. 142, pp. 476–485, 1996. [4] A. Parizzi, “Potential of an Automatic Grounding Zone Characterization Using Wrapped InSAR Phase,” in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA: IEEE, Sep. 2020, pp. 802–805, ISBN: 978-1-72816-374-1. DOI: 10.1109/IGARSS39084.2020.9323199. [5] Y. Mohajerani, S. Jeong, B. Scheuchl, I. Velicogna, E. Rignot, and P. Milillo, “Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning,” Scientific reports, vol. 11, no. 1, pp. 1–10, 2021. [6] S. Xie and Z. Tu, “Holistically-nested edge detection,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1395–1403. [7] A. Groh, Product user guide (pug) for the antarctic ice sheet cci project of esa’s climate change initiative, version 1.0, 2021. [Online]. Available: https://climate.esa.int/media/documents/ST-UL-ESA-AISCCI-PUG-0001.pdf. [8] M. Huber, Tandem-x polardem product description, prepared by german remote sensing data center (dfd) and earth observation center, 2020. [Online]. Available: https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid11882/20871_read-66374. [9] T. Nagler, H. Rott, M. Hetzenecker, J. Wuite, and P. Potin, “The sentinel-1 mission: New opportunities for ice sheet observations,” Remote Sensing, vol. 7, no. 7, pp. 9371–9389, 2015. [10] L. Padman, S. Erofeeva, and H. Fricker, “Improving antarctic tide models by assimilation of icesat laser altimetry over ice shelves,” Geophysical Research Letters, vol. 35, no. 22, 2008. [11] E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, et al., “The ncep/ncar 40-year reanalysis project,” Bulletin of the American meteorological Society, vol. 77, no. 3, pp. 437–472, 1996. [12] J. Avbelj, R. Muller, and R. Bamler, “A metric for polygon comparison and building extraction evaluation,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 1, pp. 170–174, 2014

Deep learning based automatic grounding line delineation in DInSAR interferograms

The grounding line is a subsurface geophysical feature that divides the grounded ice sheet and floating ice shelf. Knowledge of its precise location is required for estimating ice sheet mass balance, as ice discharged from the interior is typically calculated at the grounding line [1], [2]. While grounding lines in Greenland have only a minimal extension, in Antarctica, they span about 75% of its coastline. The bending of ice shelves due to ocean tides causes them to migrate several kilometers over a tidal cycle within a transition region called the grounding zone. This short-term displacement adds to the difficulty in grounding line detection on a featureless ice surface. Nevertheless, various remote sensing methods can currently detect grounding lines on a continental scale. In particular, Differential Interferometric Synthetic Aperture Radar (DInSAR) is used to measure the deformation which occurs at the grounding line due to tidal flexure of ice shelves with sub-centimeter accuracy [3]. If coherence is preserved between the SAR repeat passes, the vertical ice deformation at the grounding zone is visible in the double difference interferogram as a dense fringe belt. The landward-most fringe is considered a good approximation of the actual grounding line. Although the generation of DInSAR interferograms is already automatized, the identification of the landward-most fringe and its digitization is still majorly performed manually by human operators. Besides being labour and time-intensive, manual delineations are inconsistent due to varying interpretations of experts in identifying the landward fringe, especially in areas with poor coherence or intricate fringe patterns. In the present study, we attempt to automate the delineation by employing a Convolutional Neural Network (CNN). We developed an automatic workflow that handles the preparation of the training feature stack, training and inference of the neural network and the post-processing of network-generated delineations. The CNN architecture is based on the Holistically-Nested Edge Detection network [4]. It was trained on 478 georeferenced DInSAR interferograms from ERS-1/2, Sentinel-1 A/B and TerraSAR-X repeat pass acquisitions and their corresponding hand-delineated grounding lines that were generated within the Grounding Line Location (GLL) product of ESA’s Climate Change Initiative (AIS cci) project [5]. The training feature stack consists of four interferogram-based features: real and imaginary components, interferometric phase and pseudo coherence (estimated by applying a boxcar filter to interferometric phase) derived from the corresponding DInSAR interferograms. A median deviation of 209 m between the network-delineated and corresponding manual GLLs was measured for the test set. The trained network delineates an interferogram in milliseconds, considerably shorter than the time required for manual delineation. We propose to automatically and efficiently expand the AIS cci GLL product by applying our trained neural network to interferograms that still need to be manually delineated. In particular, we plan to generate DInSAR interferograms from highly coherent TerraSAR-X data triplets acquired in 2021 using the Integrated Wide Area Processor (IWAP) [6]. These acquisitions were made over Southern Byrd, Amundsen, Lennox-King and Dickey glaciers feeding into the Ross Ice Shelf and Recovery Glacier situated in the Ronne-Filchner Ice Shelf at high latitudes, which Sentinel-1 cannot image. Consequently, no updated grounding lines for these glaciers exist in current DInSAR-based grounding line datasets [7]. In general, the performance of our trained neural network is not dependent on the SAR sensor but on the quality of the interferograms. The automatic delineation can create monthly or half-yearly average GLL time series from all suitable DInSAR interferograms in a certain period. This derived product has a downstream application in analyzing short and long-term migratory patterns of grounding lines. References [1] C. Schoof, “Ice sheet grounding line dynamics: Steady states, stability, and hysteresis,” Journal of Geophysical Research: Earth Surface, vol. 112, no. F3, 2007. [2] E. Rignot and H. Thomas, “Mass balance of polar ice sheets,” Science, vol. 297, no. 5586, pp. 1502–1506, 2002. DOI: 10.1126/science.1073888. eprint: https://www.science.org/doi/pdf/10.1126/science.1073888. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.1073888. [3] E. Rignot, “Tidal motion, ice velocity and melt rate of petermann gletscher, greenland, measured from radar interferometry,” Journal of Glaciology, vol. 42, no. 142, pp. 476–485, 1996. [4] S. Xie and Z. Tu, “Holistically-nested edge detection,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1395–1403. [5] A. Groh, Product user guide (pug) for the antarctic ice sheet cci project of esa’s climate change initiative, version 1.0, 2021. [Online]. Available: https://climate.esa.int/media/documents/ST- UL- ESA- AISCCI- PUG- 0001.pdf. [6] F. R. Gonzalez, N. Adam, A. Parizzi, and R. Brcic, “The integrated wide area processor (iwap): A processor for wide area persistent scatterer interferometry,” in ESA Living Planet Symposium, vol. 722, 2013, p. 353. [7] E. Rignot, J. Mouginot, and B. Scheuchl, “Measures antarctic grounding line from differential satellite radar interferometry, version 2,” NASA, 2016. [Online]. Available: https://doi.org/10.5067/IKBWW4RYHF1Q

Building outline extraction from aerial imagery and digital surface model with a frame field learning framework

Deep learning-based semantic segmentation models for building delineation face the challenge of producing precise and regular building outlines. Recently, a building delineation method based on frame field learning was proposed by Girard et al. (2020) to extract regular building footprints as vector polygons directly from aerial RGB images. A fully convolution network (FCN) is trained to learn simultaneously the building mask, contours, and frame field followed by a polygonization method. With the direction information of the building contours stored in the frame field, the polygonization algorithm produces regular outlines accurately detecting edges and corners. This paper investigated the contribution of elevation data from the normalized digital surface model (nDSM) to extract accurate and regular building polygons. The 3D information provided by the nDSM overcomes the aerial images’ limitations and contributes to distinguishing the buildings from the background more accurately. Experiments conducted in Enschede, the Netherlands, demonstrate that the nDSM improves building outlines’ accuracy, resulting in better-aligned building polygons and prevents false positives. The investigated deep learning approach (fusing RGB + nDSM) results in a mean intersection over union (IOU) of 0.70 in the urban area. The baseline method (using RGB only) results in an IOU of 0.58 in the same area. A qualitative analysis of the results shows that the investigated model predicts more precise and regular polygons for large and complex structures

An IR-based Approach Towards Automated Integration of Geo-spatial Datasets in Map-based Software Systems

Data is arguably the most valuable asset of the modern world. In this era, the success of any data-intensive solution relies on the quality of data that drives it. Among vast amount of data that are captured, managed, and analyzed everyday, geospatial data are one of the most interesting class of data that hold geographical information of real-world phenomena and can be visualized as digital maps. Geo-spatial data is the source of many enterprise solutions that provide local information and insights. In order to increase the quality of such solutions, companies continuously aggregate geospatial datasets from various sources. However, lack of a global standard model for geospatial datasets makes the task of merging and integrating datasets difficult and error-prone. Traditionally, domain experts manually validate the data integration process by merging new data sources and/or new versions of previous data against conflicts and other requirement violations. However, this approach is not scalable and is hinder toward rapid release, when dealing with frequently changing big datasets. Thus more automated approaches with limited interaction with domain experts is required. As a first step to tackle this problem, in this paper, we leverage Information Retrieval (IR) and geospatial search techniques to propose a systematic and automated conflict identification approach. To evaluate our approach, we conduct a case study in which we measure the accuracy of our approach in several real-world scenarios and we interview with software developers at Localintel Inc. (our industry partner) to get their feedbacks.Comment: ESEC/FSE 2019 - Industry trac

BUILDING BOUNDARY EXTRACTION FROM LIDAR DATA USING A LOCAL ESTIMATED PARAMETER FOR ALPHA SHAPE ALGORITHM

The α-shape algorithm is a very common option to extract building boundaries from LiDAR data. This algorithm is normally executed in 2D space considering a parameter α as a binary classifier which controls the distinctiveness of points whether or not they belong to the object boundary. For point cloud data, this parameter is directly related to the local point density and the level of detail of building boundaries. Studies that have explored this concept usually consider a unique parameter α to extract all buildings in the dataset. However, the point density can have a considerable variation along the point cloud and, in this case, the use a global parameter may not be the best choice. Alternatively, this paper proposes a data-driven method that estimates a local parameter for each building. The method evaluation considered six test areas with different levels of complexity, selected from a LiDAR dataset acquired over the city of Presidente Prudente/Brazil. From the qualitative and quantitative analysis, it could be seen that the proposed method generated better results than when a global parameter is used. The proposed method was also able to withstand density variation among the LiDAR data, having a positional accuracy around 0.22 m, against 0.40 m of global parameter

Building polygon extraction from aerial images and digital surface models with a frame field learning framework

Deep learning-based models for building delineation from remotely sensed images face the challenge of producing precise and regular building outlines. This study investigates the combination of normalized digital surface models (nDSMs) with aerial images to optimize the extraction of building polygons using the frame field learning method. Results are evaluated at pixel, object, and polygon levels. In addition, an analysis is performed to assess the statistical deviations in the number of vertices of building polygons compared with the reference. The comparison of the number of vertices focuses on finding the output polygons that are the easiest to edit by human analysts in operational applications. It can serve as guidance to reduce the post-processing workload for obtaining high-accuracy building footprints. Experiments conducted in Enschede, the Netherlands, demonstrate that by introducing nDSM, the method could reduce the number of false positives and prevent missing the real buildings on the ground. The positional accuracy and shape similarity was improved, resulting in better-aligned building polygons. The method achieved a mean intersection over union (IoU) of 0.80 with the fused data (RGB + nDSM) against an IoU of 0.57 with the baseline (using RGB only) in the same area. A qualitative analysis of the results shows that the investigated model predicts more precise and regular polygons for large and complex structures

Building modeling from airborne laser scanning point clouds of low density

A Laser scanning is a relatively recent remote sensing method which nevertheless quickly gained a prominent position, especially in the area of building detection and 3D modeling. Methods for building detection and 3D modeling initially used model-driven approaches which compare a laser scanning point cloud to a set of predefined building models. A method for determining building roof types using such approaches was presented in the article of Hofman, Potůčková (2012). An important advantage of model-driven approaches is their relative robustness to various data deficiencies such as low point density or low spatial accuracy. However, output of such methods is limited to a predefined set of building models and does not allow for diversity of actual buildings. For this reason, approaches used almost exclusively nowadays are data-driven. These methods search in datasets for a set of primitives (mostly roof planes) that are subsequently used to form the final model. This approach benefits from universality of resulting models but requires generally high data quality, especially in respect to input point cloud densities. The study of Hofman, Potůčková (2017) presented a data-driven method that can reliably detect buildings even in a very sparse point cloud in spite of using data-driven approach. At a density of...A Laserové skenování je relativně mladá metoda dálkového průzkumu Země, která si ale rychle získala významné postavení zejména v oblasti detekce a modelování budov a dalších výškových objektů. Metody pro detekování a 3D modelování budov zpočátku využívaly zejména přístupů "řízených modelem" (model-driven), které porovnávají rozložení mračna laserových bodů se sadou předdefinovaných modelů. Metoda určující typ střešního pláště pomocí takového přístupu byla představena v článku Hofman, Potůčková (2012). Velikou výhodou přístupu řízeného modelem je relativní odolnost vůči nedostatkům dat, zejména nízké hustotě bodového mračna, polohové nepřesnosti bodů atd. Naopak nedostatkem těchto metod je omezení výstupu na přednastavenou sadu modelů, která nemůže obsáhnout rozmanitost reálných budov. Z tohoto důvodu se v současnosti téměř výhradně používá přístupů "řízených daty" (data-driven). Tyto metody hledají v datech pouze sadu primitiv, nejčastěji střešních rovin, ze kterých se výsledný model dodatečně skládá. Zásadním přínosem je mnohem vyšší univerzálnost výsledných modelů. Naopak nevýhodou jsou obecně vyšší nároky na kvalitu dat, zejména hustotu bodového mračna. Ve studii Hofman, Potůčková (2017) byla představena metoda, která ačkoliv využívá přístupu řízeného daty, dokáže spolehlivě detekovat budovy i ve velmi...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Spatial analysis, quantification and evaluation of developments in settlement structure based on topographic geodata

As the global population continues to grow, urbanization is one of the most significant anthropogenic processes linked to ecological change. But even in countries where the overall population is stagnating, migratory movements toward urban centres will continue to place pressure on the finite resource of land. Therefore, it is particularly important to determine and describe the development of settlement areas as precisely as possible in order to inform spatial planning decisions. For this reason, this dissertation presents vector-based methods to analyse, quantify and evaluate small-scale changes in settlement area. In this work, which constitutes a cumulative dissertation, novel methods are described that can be used to determine not only areal change in settlement and traffic areas (SuV), but also the type of building change and urban densification. This is of particular interest for the spatial planning of expanding metropolitan areas, where the question arises: Where, how and to which extent can built-up areas be further densified in order to reduce the consumption of land for new settlement areas? The methods presented here can facilitate spatially detailed analyses and already form the basis for a nationwide monitoring of settlement and open space development. This work shows how geometric deviations and changes in the underlying data model can be taken into account when determining SuV growth from data of the Authoritative Topographic-Cartographic Information System (ATKIS). In this context, positional inaccuracies of linearly and arealy modelled geometries are each treated in a special way so that minor positional offsets no longer affect the SuV increase. In addition, changes in the data model are accommodated by disregarding specific object reallocations when determining the SuV increase. To test these methods, the SuV increase was determined and analysed for Germany using national ATKIS data sets that feature geometric positional inaccuracies and data model changes. It could be shown that a considerable share of the calculated SuV increase is not due to real-world changes but to modelling issues. Furthermore, a novel method for the detection of building changes is presented, which focuses on the differentiation between modified and replaced buildings. It could be shown that this new approach is more accurate than other investigated methods. Furthermore, an algorithm was developed in this work to generate defined location deviations. This could be used to show how position deviations affect the accuracy of the examined procedures. The threshold values determined in this work can form the basis for similar investigations. In addition, an indicator was developed to track changes in building density. This indicator not only reflects the extent of building change but also the size of the existing building stock. Moreover, the indicator was designed in such a way as to allow comparison of the densification of developed and undeveloped areas, and thus also inner and outer urban areas. Furthermore, the indicator can be used to symmetrically calculate a decrease in the building stock, enabling a comparison of densification and de-densification processes.:1. Introduction 1.1 Motivation 1.2 Problem description 1.3 Aims 1.4 Structure 2. Dissertation main articles 2.1 Measuring land take in Germany 2.2 Detecting building change 2.3 Indicator for building densification 3. Methods for measuring settlement changes 3.1 Measuring changes through land use data 3.2 Detection of building changes 3.3 Measuring changes in building density 4. Main findings 4.1 Effects of non-real changes on land take 4.2 Distinguishing building modification and replacement 4.3 Impact of building changes on building density 4.4 How the articles are connected 4.5 Additional relevant publications 5. Conclusion and Outlook References Abbreviations List of figures List of author’s publications Articles Conference Papers Acknowledgments Appendix with publication

Automatic Extraction of Tall Buildings from Off-Nadir High Resolution Satellite Images Using Model-Based Approach

학위논문 (석사)-- 서울대학교 대학원 : 건설환경공학부, 2015. 2. 김용일.최근 다양한 고해상도 지구관측위성이 발사 되고, 고해상도 위성영상의 상업적인 보급이 활발해 짐에 따라 이를 이용한 다양한 연구들이 이루어지고 있다. 특히 1m 이하의 높은 공간해상도는 지상에 위치한 건물, 도로, 차량 등 다양한 물체에 관한 정보를 제공하고 있으며, 영상으로부터 건물의 2차원 정보를 추출하는 연구는 도시 모니터링, 재난관리 등의 분야에 사용될 수 있어 필요성이 대두되고 있다. 그러나 건물 추출 정확도에 영향을 미치는 요소가 다양하여 대다수의 건물 추출 연구가 연직영상을 사용한 저층 건물 추출에 제한되어 있다. 이러한 기존 연구를 이용하여 비연직 방향으로 촬영된 고층건물을 추출하는 데는 한계가 따르며, 이는 다양한 제원의 영상을 이용하여 다양한 높이의 건물을 추출하는데 어려움이 존재하게 만든다. 따라서 본 연구는 비연직 영상에서 고층건물의 상단을 자동으로 추출하는 알고리즘을 제안하여 기존 연구의 한계를 극복하고자 하였다. 제안하는 알고리즘은 고층건물 영역 자동 추출과 고층건물 상단 추출의 두 단계로 구분된다. 건물영역 자동 탐지 과정에서는 Otsu 기법과 영역확장 기법을 사용하여 그림자 영상과 건물 영역을 자동으로 추출한다. 추출된 두 영역과 영상의 메타데이터, 에지 정보를 이용하여 고층건물 상단의 선을 실제 건물 선에 최적화시킨 후, 건물의 구조적 특징과 영역적인 특징을 반영한 모델 기반 기법을 통해 고층건물 상단영역을 자동으로 완성하였다. 제안 방법을 주거지구와 업무지구의 IKONOS-2, QuickBird-2 영상에 적용하여 알고리즘의 우수성을 검증하였다. 화소 및 객체 기반의 정확도 분석 결과, 모든 경우에 대하여 사용자 정확도는 0.87, 생산자 정확도는 0.79, 그리고 F 측정치는 0.83 이상으로 나타나 영상의 종류와 실험 지역의 속성과 무관하게 알고리즘이 유용함을 보여주었다. 또한 객체 기반의 평균 F 측정치는 0.89로 나타났으며, 이는 기존 건물 추출 연구와 비교하여 비슷하거나 높았다. 본 연구에서는 흑백의 단영상만을 사용하여 다중 분광 영상이나 부가 데이터를 사용하는 기존의 연구에 비해 비용 효율적인 기법을 제안한다. 비연직 영상에서 고층건물의 상단을 다른 면과 구분하는 자동화된 방법을 제안하여 기존 건물 추출의 한계를 극복하고 고해상도 영상으로부터 고층건물의 정보를 추출할 수 있는 방안을 제시하였다. 기법의 우수성을 바탕으로, 제안 기법은 다양한 도시 지역의 고층건물 상단을 추출하는 연구에 적용될 수 있을 뿐만 아니라 건물 상단 간의 매칭을 통한 3차원 건물 모델 생성, 도시건물변화탐지 등의 분야에 적용될 수 있다. 이는 추출될 수 있는 건물 정보를 다양화하여 영상을 이용한 건물 추출 분야가 더욱 발전할 수 있는 기반을 제공한다.1. 서론 1 1.1 연구 배경 및 동기 1 1.2 연구동향 2 1.3 연구의 목적 및 범위 7 2. 고층건물 영역 자동 추출 11 2.1 영상 전처리 12 2.2 Otsu 기법을 이용한 건물 그림자 영역 추출 15 2.3 영역확장 기법을 이용한 건물 영역 추출 16 2.3.1 영역확장 기법을 위한 초기 시드 추출 16 2.3.2 고층건물 영역 중첩 및 오추출 제거 18 3. 고층건물 상단 추출 21 3.1 고층건물 상단 선 추출 23 3.1.1 LSD를 이용한 영상 내 초기 건물 영역 선 추출 23 3.1.2 고층건물 상단 영역 선 추출 25 3.2 고층건물 상단 선 최적화 32 3.3 고층건물 상단 영역 추출 36 3.3.1 수직관계를 이용한 건물 상단 영역 추출 36 3.3.2 평행관계를 이용한 건물 상단 영역 추출 39 3.3.3 추출된 건물 상단 영역 통합 및 최적화 43 4. 실험 및 적용 47 4.1 실험 지역 및 자료 47 4.2 실험 결과 48 4.2.1 고층건물 영역 자동 추출 결과 48 4.2.2 고층건물 상단 추출 결과 53 4.2.2.1 고층건물 상단 선 추출 및 최적화 결과 53 4.2.2.2 고층건물 상단 영역 추출 결과 59 5. 결론 71 6. 참고문헌 74Maste