BAYESIAN PREDICTION METHOD FOR SHADOW DETECTION AND RECONSTRUCTION IN HSR IMAGES USING MORPHOLOGICAL FILTER

Several approaches are exists today according to color, intensity and saturation value etc that are very less accurate. Within this paper, we advise alternative shadow recognition formula according to thresholding and morphological filtering, along with an alternate shadow renovation formula in line with the example learning method and Markov random field (MRF). The primary purpose of this project is recognition and renovation of shadows from VHSR images. Removing or alleviating the instants while using shadows in HSR images for more processing is an extremely important task because the shadows are induce to loss or miss conjecture of radiometric information and induce to image interpretation. Throughout the shadow recognition procedure, the bimodal distributions of pixel values within the near-infrared (NIR) band and also the panchromatic band are adopted for thresholding. Throughout the shadow renovation procedure, we model the connection between non shadow and also the corresponding shadow pixels and between neighboring no shadow pixels by using MRF. With extension for this paper we advise Bayesian conjecture way of accurate conjecture of shadow. Within this paper for accurate shadow recognition we combine thresholding and morphological filtering concepts. This shadow recognition includes Thresholding, Morphological filtering and edge compensation stages

BAYESIAN PREDICTION METHOD FOR SHADOW DETECTION AND RECONSTRUCTION IN HSR IMAGES USING MORPHOLOGICAL FILTER

Several approaches are exists today according to color, intensity and saturation value etc that are very less accurate. Within this paper, we advise alternative shadow recognition formula according to thresholding and morphological filtering, along with an alternate shadow renovation formula in line with the example learning method and Markov random field (MRF). The primary purpose of this project is recognition and renovation of shadows from VHSR images. Removing or alleviating the instants while using shadows in HSR images for more processing is an extremely important task because the shadows are induce to loss or miss conjecture of radiometric information and induce to image interpretation. Throughout the shadow recognition procedure, the bimodal distributions of pixel values within the near-infrared (NIR) band and also the panchromatic band are adopted for thresholding. Throughout the shadow renovation procedure, we model the connection between non shadow and also the corresponding shadow pixels and between neighboring no shadow pixels by using MRF. With extension for this paper we advise Bayesian conjecture way of accurate conjecture of shadow. Within this paper for accurate shadow recognition we combine thresholding and morphological filtering concepts. This shadow recognition includes Thresholding, Morphological filtering and edge compensation stages

High-resolution optical and SAR image fusion for building database updating

This paper addresses the issue of cartographic database (DB) creation or updating using high-resolution synthetic aperture radar and optical images. In cartographic applications, objects of interest are mainly buildings and roads. This paper proposes a processing chain to create or update building DBs. The approach is composed of two steps. First, if a DB is available, the presence of each DB object is checked in the images. Then, we verify if objects coming from an image segmentation should be included in the DB. To do those two steps, relevant features are extracted from images in the neighborhood of the considered object. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of Dempster–Shafer evidence theory

Graph Search and its Application in Building Extraction from High Resolution Remote Sensing Imagery

Building extraction using Hough transformation and cycle detection

BUILDING DETECTION USING AERIAL IMAGES AND DIGITAL SURFACE MODELS

In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW) method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM) released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method

Evaluation of automatic building detection approaches combining high resolution images and LiDAR data

In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered. © 2011 by the authors.The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation and FEDER in the framework of the projects CGL2009-14220 and CGL2010-19591/BTE, and the support of the Spanish Instituto Geografico Nacional (IGN).Hermosilla, T.; Ruiz Fernández, LÁ.; Recio Recio, JA.; Estornell Cremades, J. (2011). Evaluation of automatic building detection approaches combining high resolution images and LiDAR data. Remote Sensing. 3:1188-1210. https://doi.org/10.3390/rs3061188S118812103Mayer, H. (1999). 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S., Shan, J., & Bethel, J. S. (2003). Class-Guided Building Extraction from Ikonos Imagery. Photogrammetric Engineering & Remote Sensing, 69(2), 143-150. doi:10.14358/pers.69.2.143STASSOPOULOU, A., & CAELLI, T. (2000). BUILDING DETECTION USING BAYESIAN NETWORKS. International Journal of Pattern Recognition and Artificial Intelligence, 14(06), 715-733. doi:10.1142/s0218001400000477Jin, X., & Davis, C. H. (2005). Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information. EURASIP Journal on Advances in Signal Processing, 2005(14). doi:10.1155/asp.2005.2196Kim, Z., & Nevatia, R. (1999). Uncertain Reasoning and Learning for Feature Grouping. Computer Vision and Image Understanding, 76(3), 278-288. doi:10.1006/cviu.1999.0803Dare, P. M. (2005). Shadow Analysis in High-Resolution Satellite Imagery of Urban Areas. Photogrammetric Engineering & Remote Sensing, 71(2), 169-177. doi:10.14358/pers.71.2.169Weidner, U., & Förstner, W. (1995). Towards automatic building extraction from high-resolution digital elevation models. ISPRS Journal of Photogrammetry and Remote Sensing, 50(4), 38-49. doi:10.1016/0924-2716(95)98236-sCord, M., & Declercq, D. (2001). Three-dimensional building detection and modeling using a statistical approach. IEEE Transactions on Image Processing, 10(5), 715-723. doi:10.1109/83.918565Ma, R. (2005). DEM Generation and Building Detection from Lidar Data. Photogrammetric Engineering & Remote Sensing, 71(7), 847-854. doi:10.14358/pers.71.7.847Miliaresis, G., & Kokkas, N. (2007). Segmentation and object-based classification for the extraction of the building class from LIDAR DEMs. Computers & Geosciences, 33(8), 1076-1087. doi:10.1016/j.cageo.2006.11.012Zhang, K., Yan, J., & Chen, S.-C. (2006). Automatic Construction of Building Footprints From Airborne LIDAR Data. IEEE Transactions on Geoscience and Remote Sensing, 44(9), 2523-2533. doi:10.1109/tgrs.2006.874137Lafarge, F., Descombes, X., Zerubia, J., & Pierrot-Deseilligny, M. (2008). Automatic building extraction from DEMs using an object approach and application to the 3D-city modeling. ISPRS Journal of Photogrammetry and Remote Sensing, 63(3), 365-381. doi:10.1016/j.isprsjprs.2007.09.003Yu, B., Liu, H., Wu, J., Hu, Y., & Zhang, L. (2010). Automated derivation of urban building density information using airborne LiDAR data and object-based method. Landscape and Urban Planning, 98(3-4), 210-219. doi:10.1016/j.landurbplan.2010.08.004Paparoditis, N., Cord, M., Jordan, M., & Cocquerez, J.-P. (1998). Building Detection and Reconstruction from Mid- and High-Resolution Aerial Imagery. Computer Vision and Image Understanding, 72(2), 122-142. doi:10.1006/cviu.1998.0722Estornell, J., Ruiz, L. A., Velázquez-Martí, B., & Hermosilla, T. (2011). Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area. International Journal of Digital Earth, 4(6), 521-538. doi:10.1080/17538947.2010.533201Ruiz, L. A., Recio, J. A., Fernández-Sarría, A., & Hermosilla, T. (2011). A feature extraction software tool for agricultural object-based image analysis. Computers and Electronics in Agriculture, 76(2), 284-296. doi:10.1016/j.compag.2011.02.007Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610-621. doi:10.1109/tsmc.1973.4309314Sutton, R. N., & Hall, E. L. (1972). Texture Measures for Automatic Classification of Pulmonary Disease. IEEE Transactions on Computers, C-21(7), 667-676. doi:10.1109/t-c.1972.223572Freund, Y. (1995). Boosting a Weak Learning Algorithm by Majority. Information and Computation, 121(2), 256-285. doi:10.1006/inco.1995.1136Shufelt, J. A. (1999). 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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