Results 161 to 170 of about 364,054 (179)
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Road Structure Refined CNN for Road Extraction in Aerial Image
IEEE Geoscience and Remote Sensing Letters, 2017In this letter, we propose a road structure refined convolutional neural network (RSRCNN) approach for road extraction in aerial images. In order to obtain structured output of road extraction, both deconvolutional and fusion layers are designed in the architecture of RSRCNN.
Yanan Wei, Zulin Wang, Mai Xu
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The Role of Grouping for Road Extraction
1997An approach to complete road networks extracted from aerial images is presented. Since low-level road extraction results will generally be fragmented and contain false hypotheses, this task involves selecting the correct roads from the extraction results as well as connecting them to construct the road network.
Helmut Mayer +2 more
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Road Network Extraction in Suburban Areas
The Photogrammetric Record, 2012AbstractIn this paper, an algorithm for the extraction of road networks in suburban areas is presented. The algorithm is region‐based and uses high‐resolution colour infrared images as well as, optionally, a digital surface model (DSM). The road extraction starts with a segmentation using the normalised cuts algorithm; afterwards the segments are ...
Anne Grote +2 more
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Context-Supported Road Extraction
1997Contextual information can facilitate automatic extraction of objects from digital imager). This paper addresses the use of context for the automatic extraction of roads from aerial imagery. Context is restricted to knowledge about relations between roads and other objects and is hierarchically structured.
Christian Heipke +4 more
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Convolutional neural network for road extraction
LIDAR Imaging Detection and Target Recognition 2017, 2017In this paper, the convolution neural network with large block input and small block output was used to extract road. To reflect the complex road characteristics in the study area, a deep convolution neural network VGG19 was conducted for road extraction.
Li Junping +4 more
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An Optimal Road Seed Extraction Algorithm
2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, 2008The proper extraction of road seeds is the premier step of road network extraction from high resolution remote sensing images. An optimal road seed extraction algorithm is proposed. Firstly, Canny-Deriche edge detection and spatial FCM (fuzzy c means) region extraction are performed separately to detect the details.Secondly, an averaged Hausdorff ...
Keju Zu, Guangyao Li, Yang Hu, K. Chehdi
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Extraction of road segments by spatial filters
Systems and Computers in Japan, 1994AbstractThis paper proposes a new approach for extracting segments from map data. In general, it is difficult to construct a system that can completely extract all road data noise free. On the other hand, it will be easy to construct a system that can extract all road data although noises are included.
Yuzo Hirai +3 more
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Road and Road Intersection Extraction
2013Beginning in 1879 the United States Geological Survey (USGS) began surveying land in the United States. Since then they have developed over 55, 000 1: 24, 000-scale topographic maps covering the 48 coterminous states in a standard, detailed manner. The result is a wealth of data contained in physical documents.
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Road Extracting Based on Texture Analysis
16th International Conference on Artificial Reality and Telexistence--Workshops (ICAT'06), 2006In this paper, an automatic method for road network extraction from high resolution aerial image is presented, the recommended input is merely the color image. The modified technique, TPA, and a new way of initial road point extraction are applied. The whole method consists of three modules: 1) the first road detection; 2) statistics and fusing; 3) the
Lou Ji-lin, Lu Yaya, Gu Hui
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Optimization based classifiers for road extraction
SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218), 2002We investigate the performance of a linear programming-based decision tree in creating gray scale images for road extraction from AVIRIS images. We apply our method for classification of pixels from a digital image of an area near Williamsburg, Virginia, using the distance from discriminant lines as a measure to create a gray scale image.
V. Chalasani, P.A. Beling
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