An Efficient Approach for Automatic Rectangular Building Extraction From Very High Resolution Optical Satellite Imagery

Wang, Jun; Yang, Xiucheng; Qin, Xuebin; Ye, Xin; Qin, Qiming

doi:10.1109/lgrs.2014.2347332

Cited by 71 publications

(12 citation statements)

References 17 publications

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“…In addition, the test images include differently sized and shaped linear buildings. The experiments with different types of satellite data demonstrate the robustness and effectiveness of the proposed algorithm to compare the results by segmentation method and by Wang’s method [11]. …”

Section: Resultsmentioning

confidence: 99%

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Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings

Yan

Qiu

et al. 2018

Sensors

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In this paper, we proposed a novel object-based dense matching method specially for the high-precision disparity map of building objects in urban areas, which can maintain accurate object structure characteristics. The proposed framework mainly includes three stages. Firstly, an improved edge line extraction method is proposed for the edge segments to fit closely to building outlines. Secondly, a fusion method is proposed for the outlines under the constraint of straight lines, which can maintain the building structural attribute with parallel or vertical edges, which is very useful for the dense matching method. Finally, we proposed an edge constraint and outline compensation (ECAOC) dense matching method to maintain building object structural characteristics in the disparity map. In the proposed method, the improved edge lines are used to optimize matching search scope and matching template window, and the high-precision building outlines are used to compensate the shape feature of building objects. Our method can greatly increase the matching accuracy of building objects in urban areas, especially at building edges. For the outline extraction experiments, our fusion method verifies the superiority and robustness on panchromatic images of different satellites and different resolutions. For the dense matching experiments, our ECOAC method shows great advantages for matching accuracy of building objects in urban areas compared with three other methods.

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Section: Resultsmentioning

confidence: 99%

“…The classical SSD (the Sum of Squared Differences), SAD (Sum of Absolute Differences), NCC (Normal Cross Correlation) all belong to local methods [11]. In the local methods, some feature-based matching constraint methods are proposed to improve the area-based dense matching accuracy [12].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings

Yan

Qiu

et al. 2018

Sensors

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“…The model is only used for gray images and is unable to detect small buildings. Wang et al [39] proposed a framework without user intervention. The model is based on line segments and a perceptual grouping strategy.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Then, the obtained edge segments are comprised of a continuous chain of edge pixels. This chain of edge pixels is divided into one or more straight line segments [39]. Our goal is to obtain the initial line and determine its current direction, which is accomplished by navigating from the pixels of the edge segment to the boundary of the top-left rectangle.…”

Section: Efficient Line Segment Detectionmentioning

confidence: 99%

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A Framework for Automatic Building Detection from Low-Contrast Satellite Images

Aamir

Rahman

et al. 2018

Symmetry

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Building detection in satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve building detection performance. Different algorithms have been proposed to extract building objects from high-resolution satellite images with standard contrast. However, building detection from low-contrast satellite images to predict symmetrical findings as of past studies using normal contrast images is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to detect buildings from low-contrast satellite images. In an effort to enhance visualization of satellite images, in this study, first, the contrast of an image is optimized to represent all the information using singular value decomposition (SVD) based on the discrete wavelet transform (DWT). Second, a line-segment detection scheme is applied to accurately detect building line segments. Third, the detected line segments are hierarchically grouped to recognize the relationship of identified line segments, and the complete contours of the building are attained to obtain candidate rectangular buildings. In this paper, the results from the method above are compared with existing approaches based on high-resolution images with reasonable contrast. The proposed method achieves high performance thus yields more diversified and insightful results over conventional techniques.

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HF-FCN: Hierarchically Fused Fully Convolutional Network for Robust Building Extraction

Zuo

Feng

Chen

2017

Computer Vision – ACCV 2016

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An Efficient Approach for Automatic Rectangular Building Extraction From Very High Resolution Optical Satellite Imagery

Cited by 71 publications

References 17 publications

Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings

Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings

A Framework for Automatic Building Detection from Low-Contrast Satellite Images

HF-FCN: Hierarchically Fused Fully Convolutional Network for Robust Building Extraction

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