The Experts below are selected from a list of 6714 Experts worldwide ranked by ideXlab platform

Jagersand Martin - One of the best experts on this subject based on the ideXlab platform.

  • accurate outline extraction of Individual Building from very high resolution optical images
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Shida He, Xiucheng Yang, Masood Dehghan, Jagersand Martin
    Abstract:

    This letter presents a novel approach for extracting accurate outlines of Individual Buildings from very high-resolution (0.1–0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph $\textit {G(V,E)}$ is constructed based on the endpoints of those extracted line segments. The Building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various Building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of Building roofs and outperforms the state-of-the-art method.

  • Accurate Outline Extraction of Individual Building From Very High-Resolution Optical Images
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Shida He, Xiucheng Yang, Masood Dehghan, Jagersand Martin
    Abstract:

    This letter presents a novel approach for extracting accurate outlines of Individual Buildings from very high-resolution (0.1-0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph G(V,E) is constructed based on the endpoints of those extracted line segments. The Building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various Building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of Building roofs and outperforms the state-of-the-art method.

Shida He - One of the best experts on this subject based on the ideXlab platform.

  • accurate outline extraction of Individual Building from very high resolution optical images
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Shida He, Xiucheng Yang, Masood Dehghan, Jagersand Martin
    Abstract:

    This letter presents a novel approach for extracting accurate outlines of Individual Buildings from very high-resolution (0.1–0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph $\textit {G(V,E)}$ is constructed based on the endpoints of those extracted line segments. The Building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various Building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of Building roofs and outperforms the state-of-the-art method.

  • Accurate Outline Extraction of Individual Building From Very High-Resolution Optical Images
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Shida He, Xiucheng Yang, Masood Dehghan, Jagersand Martin
    Abstract:

    This letter presents a novel approach for extracting accurate outlines of Individual Buildings from very high-resolution (0.1-0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph G(V,E) is constructed based on the endpoints of those extracted line segments. The Building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various Building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of Building roofs and outperforms the state-of-the-art method.

Fangling Pu - One of the best experts on this subject based on the ideXlab platform.

  • Individual Building extraction from terrasar x images based on ontological semantic analysis
    Remote Sensing, 2016
    Co-Authors: Xin Xu, Hao Dong, Chao Song, Fangling Pu
    Abstract:

    Accurate Building information plays a crucial role for urban planning, human settlements and environmental management. Synthetic aperture radar (SAR) images, which deliver images with metric resolution, allow for analyzing and extracting detailed information on urban areas. In this paper, we consider the problem of extracting Individual Buildings from SAR images based on domain ontology. By analyzing a Building scattering model with different orientations and structures, the Building ontology model is set up to express multiple characteristics of Individual Buildings. Under this semantic expression framework, an object-based SAR image segmentation method is adopted to provide homogeneous image objects, and three categories of image object features are extracted. Semantic rules are implemented by organizing image object features, and the Individual Building objects expression based on an ontological semantic description is formed. Finally, the Building primitives are used to detect Buildings among the available image objects. Experiments on TerraSAR-X images of Foshan city, China, with a spatial resolution of 1.25 m × 1.25 m, have shown the total extraction rates are above 84%. The results indicate the ontological semantic method can exactly extract flat-roof and gable-roof Buildings larger than 250 pixels with different orientations.

Xiucheng Yang - One of the best experts on this subject based on the ideXlab platform.

  • accurate outline extraction of Individual Building from very high resolution optical images
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Shida He, Xiucheng Yang, Masood Dehghan, Jagersand Martin
    Abstract:

    This letter presents a novel approach for extracting accurate outlines of Individual Buildings from very high-resolution (0.1–0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph $\textit {G(V,E)}$ is constructed based on the endpoints of those extracted line segments. The Building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various Building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of Building roofs and outperforms the state-of-the-art method.

  • Accurate Outline Extraction of Individual Building From Very High-Resolution Optical Images
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Shida He, Xiucheng Yang, Masood Dehghan, Jagersand Martin
    Abstract:

    This letter presents a novel approach for extracting accurate outlines of Individual Buildings from very high-resolution (0.1-0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph G(V,E) is constructed based on the endpoints of those extracted line segments. The Building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various Building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of Building roofs and outperforms the state-of-the-art method.

Masood Dehghan - One of the best experts on this subject based on the ideXlab platform.

  • accurate outline extraction of Individual Building from very high resolution optical images
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Shida He, Xiucheng Yang, Masood Dehghan, Jagersand Martin
    Abstract:

    This letter presents a novel approach for extracting accurate outlines of Individual Buildings from very high-resolution (0.1–0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph $\textit {G(V,E)}$ is constructed based on the endpoints of those extracted line segments. The Building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various Building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of Building roofs and outperforms the state-of-the-art method.

  • Accurate Outline Extraction of Individual Building From Very High-Resolution Optical Images
    IEEE Geoscience and Remote Sensing Letters, 2018
    Co-Authors: Shida He, Xiucheng Yang, Masood Dehghan, Jagersand Martin
    Abstract:

    This letter presents a novel approach for extracting accurate outlines of Individual Buildings from very high-resolution (0.1-0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph G(V,E) is constructed based on the endpoints of those extracted line segments. The Building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various Building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of Building roofs and outperforms the state-of-the-art method.