A new straight line reconstruction methodology from multi-spectral stereo aerial images

In this study, a new methodology for the reconstruction of line features from multispectral stereo aerial images is presented. We take full advantage of the existing multispectral information in aerial images all over the steps of pre-processing and edge detection. To accurately describe the straight line segments, a principal component analysis technique is adapted. The line to line correspondences between the stereo images are established using a new pair-wise stereo matching approach. The approach involves new constraints, and the redundancy inherent in pair relations gives us a possibility to reduce the number of false matches in a probabilistic manner. The methodology is tested over three different urban test sites and provided good results for line matching and reconstruction

Automatic building extraction from DEMs using an object approach and application to the 3D-city modeling

International audienceIn this paper, we present an automatic building extraction method from Digital Elevation Models based on an object approach. First, a rough approximation of the building footprints is realized by a method based on marked point processes: the building footprints are modeled by rectangle layouts. Then, these rectangular footprints are regularized by improving the connection between the neighboring rectangles and detecting the roof height discontinuities. The obtained building footprints are structured footprints: each element represents a specific part of an urban structure. Results are finally applied to a 3D-city modeling process

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

Semi-Automated DIRSIG scene modeling from 3D lidar and passive imagery

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is an established, first-principles based scene simulation tool that produces synthetic multispectral and hyperspectral images from the visible to long wave infrared (0.4 to 20 microns). Over the last few years, significant enhancements such as spectral polarimetric and active Light Detection and Ranging (lidar) models have also been incorporated into the software, providing an extremely powerful tool for multi-sensor algorithm testing and sensor evaluation. However, the extensive time required to create large-scale scenes has limited DIRSIG’s ability to generate scenes ”on demand.” To date, scene generation has been a laborious, time-intensive process, as the terrain model, CAD objects and background maps have to be created and attributed manually. To shorten the time required for this process, this research developed an approach to reduce the man-in-the-loop requirements for several aspects of synthetic scene construction. Through a fusion of 3D lidar data with passive imagery, we were able to semi-automate several of the required tasks in the DIRSIG scene creation process. Additionally, many of the remaining tasks realized a shortened implementation time through this application of multi-modal imagery. Lidar data is exploited to identify ground and object features as well as to define initial tree location and building parameter estimates. These estimates are then refined by analyzing high-resolution frame array imagery using the concepts of projective geometry in lieu of the more common Euclidean approach found in most traditional photogrammetric references. Spectral imagery is also used to assign material characteristics to the modeled geometric objects. This is achieved through a modified atmospheric compensation applied to raw hyperspectral imagery. These techniques have been successfully applied to imagery collected over the RIT campus and the greater Rochester area. The data used include multiple-return point information provided by an Optech lidar linescanning sensor, multispectral frame array imagery from the Wildfire Airborne Sensor Program (WASP) and WASP-lite sensors, and hyperspectral data from the Modular Imaging Spectrometer Instrument (MISI) and the COMPact Airborne Spectral Sensor (COMPASS). Information from these image sources was fused and processed using the semi-automated approach to provide the DIRSIG input files used to define a synthetic scene. When compared to the standard manual process for creating these files, we achieved approximately a tenfold increase in speed, as well as a significant increase in geometric accuracy

Statistical Fusion of Multi-aspect Synthetic Aperture Radar Data for Automatic Road Extraction

In this dissertation, a new statistical fusion for automatic road extraction from SAR images taken from different looking angles (i.e. multi-aspect SAR data) was presented. The main input to the fusion is extracted line features. The fusion is carried out on decision-level and is based on Bayesian network theory

Automatic reconstruction of three-dimensional building models from dense image matching datasets

PhD ThesisThe generation of three-dimensional (3D) building models without roof geometry is currently easily automated using a building footprint and single height value. The automatic reconstruction of roof structures, however, remains challenging because of the complexity and variability in building geometry. Attempts from imagery have utilised high spatial resolution but have only reconstructed simple geometry. This research addresses the complexity of roof geometry reconstruction by developing an approach, which focuses on the extraction of corners to reconstruct 3D buildings as boundary representation models, to try overcome the limitations of planar fitting procedures, which are currently favoured. Roof geometry information was extracted from surface models, true orthophotos and photogrammetric point clouds; reconstructed at the same spatial resolution of the captured aerial imagery, with developments in pixel-to-pixel matching. Edges of roof planes were extracted by the Canny edge detector, and then refined with a workflow based on the principles of scan-line segmentation to remove false positive detection. Line tracing procedures defined the corner positions of the extracted edges. A connectivity ruleset was developed, which searches around the endpoints of unconnected lines, testing for potential connecting corners. All unconnected lines were then removed reconstruct 3D models as a closed network of connecting roof corners. Building models have been reconstructed both as block models and also with roof structures. The methodology was tested on data of Newcastle upon Tyne, United Kingdom, with results showing corner extraction success at 75% and to within a planimetric accuracy of ±0.5 m. The methodology was then tested on data of Vaihingen, Germany, which forms part of the ISPRS 3D reconstruction benchmark. This allowed direct comparisons to be made with other methods. The results from both study areas showed similar planimetric accuracy of extracted corners. However, both sites were not as successful in the reconstruction of roof planes.Ordnance Surve

Merging digital surface models sourced from multi-satellite imagery and their consequent application in automating 3D building modelling

Recently, especially within the last two decades, the demand for DSMs (Digital Surface Models) and 3D city models has increased dramatically. This has arisen due to the emergence of new applications beyond construction or analysis and consequently to a focus on accuracy and the cost. This thesis addresses two linked subjects: first improving the quality of the DSM by merging different source DSMs using a Bayesian approach; and second, extracting building footprints using approaches, including Bayesian approaches, and producing 3D models. Regarding the first topic, a probabilistic model has been generated based on the Bayesian approach in order to merge different source DSMs from different sensors. The Bayesian approach is specified to be ideal in the case when the data is limited and this can consequently be compensated by introducing the a priori. The implemented prior is based on the hypothesis that the building roof outlines are specified to be smooth, for that reason local entropy has been implemented in order to infer the a priori data. In addition to the a priori estimation, the quality of the DSMs is obtained by using field checkpoints from differential GNSS. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the Maximum Likelihood model which showed similar quantitative statistical results and better qualitative results. Perhaps it is worth mentioning that, although the DSMs used in the merging have been produced using satellite images, the model can be applied on any type of DSM. The second topic is building footprint extraction based on using satellite imagery. An efficient flow-line for automatic building footprint extraction and 3D model construction, from both stereo panchromatic and multispectral satellite imagery was developed. This flow-line has been applied in an area of different building types, with both hipped and sloped roofs. The flow line consisted of multi stages. First, data preparation, digital orthoimagery and DSMs are created from WorldView-1. Pleiades imagery is used to create a vegetation mask. The orthoimagery then undergoes binary classification into ‘foreground’ (including buildings, shadows, open-water, roads and trees) and ‘background’ (including grass, bare soil, and clay). From the foreground class, shadows and open water are removed after creating a shadow mask by thresholding the same orthoimagery. Likewise roads have been removed, for the time being, after interactively creating a mask using the orthoimagery. NDVI processing of the Pleiades imagery has been used to create a mask for removing the trees. An ‘edge map’ is produced using Canny edge detection to define the exact building boundary outlines, from enhanced orthoimagery. A normalised digital surface model (nDSM) is produced from the original DSM using smoothing and subtracting techniques. Second, start Building Detection and Extraction. Buildings can be detected, in part, in the nDSM as isolated relatively elevated ‘blobs’. These nDSM ‘blobs’ are uniquely labelled to identify rudimentary buildings. Each ‘blob’ is paired with its corresponding ‘foreground’ area from the orthoimagery. Each ‘foreground’ area is used as an initial building boundary, which is then vectorised and simplified. Some unnecessary details in the ‘edge map’, particularly on the roofs of the buildings can be removed using mathematical morphology. Some building edges are not detected in the ‘edge map’ due to low contrast in some parts of the orthoimagery. The ‘edge map’ is subsequently further improved also using mathematical morphology, leading to the ‘modified edge map’. Finally, A Bayesian approach is used to find the most probable coordinates of the building footprints, based on the ‘modified edge map’. The proposal that is made for the footprint a priori data is based on the creating a PDF which assumes that the probable footprint angle at the corner is 90o and along the edge is 180o, with a less probable value given to the other angles such as 45o and 135o. The 3D model is constructed by extracting the elevation of the buildings from the DSM and combining it with the regularized building boundary. Validation, both quantitatively and qualitatively has shown that the developed process and associated algorithms have successfully been able to extract building footprints and create 3D models