Extraction of building roof contours from the integration of high-resolution aerial imagery and laser data using Markov random fields

Fernandes, Vanessa Jordão Marcato; Póz, Aluir Porfírio Dal

doi:10.1080/19479832.2018.1487885

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…To solve urban road centreline problems, Raziq et al [10] proposed a morphological method to extract the road centreline of the urban environment by combining the automatic threshold and morphological operation techniques. Vanessa et al [11] proposed a building roof contours automatic extraction method by integrating airborne laser scanning and photogrammetric data based on the Markov random field probabilistic approach. However, the above methods heavily rely on manual feature design and implementations, making it difficult to obtain the deep semantic features and detailed features contained in RSIs.…”

Section: Introductionmentioning

confidence: 99%

Multi-channel recurrent attention network for building extraction from high resolution remote sensing images

Wang¹,

Xu²,

Wang³

et al. 2022

Meas. Sci. Technol.

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Building extraction from high-resolution remote sensing images is of great importance for urban planning, disaster assessment, and geography mapping. In recent years, convolutional neural networks (CNNs) have made outstanding achievements in improving the precision of building extraction. However, most existing approaches have some problems, such as insufficient detailed feature extraction and ignorance of the relationship between different features. In this study, we propose a novel multi-channel recurrent attention network (MCANet) for building extraction. Firstly, the multi-scale channel attention mechanism (MS-CAM) is used to expand the convolution kernel receptive field, making the model can extract rich building region feature information. Secondly, we use the spatial pyramid recurrent block (SPR-Block) to establish long-range dependencies over space, channel, and layer of different convolutions. Finally, the multi-channel feature fusion block (MCFF-Block) is used to fuse the multi-scale channel features information, and improve the building extraction precision. Experimental results show that the proposed MCANet achieves better results (recall, precision, IoU, and F1_score on the IAILD dataset are 89.82, 94.38%, 87.42%, and 88.25%, respectively), and outperforms the other state-of-the-art (SOTA) approaches.

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Section: Introductionmentioning

confidence: 99%

Multi-channel recurrent attention network for building extraction from high resolution remote sensing images

Wang¹,

Xu²,

Wang³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…However, the boundaries' accuracy in these cases can be compromised due to the LiDAR point cloud sparsity. Therefore, many authors have explored the synergy between aerial images and LiDAR data to improve the building extraction process [3,[13][14][15][16]. Nevertheless, the efficient integration of these two data sources for building extraction still needs improvement.…”

Section: Introductionmentioning

confidence: 99%

Snake-Based Model for Automatic Roof Boundary Extraction in the Object Space Integrating a High-Resolution Aerial Images Stereo Pair and 3D Roof Models

et al. 2021

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The accelerated urban development over the last decades has made it necessary to update spatial information rapidly and constantly. Therefore, cities' three-dimensional models have been widely used as a study base for various urban problems. However, although many efforts have been made to develop new building extraction methods, reliable and automatic extraction is still a major challenge for the remote sensing and computer vision communities, mainly due to the complexity and variability of urban scenes. This paper presents a method to extract building roof boundaries in the object space by integrating a high-resolution aerial images stereo pair, three-dimensional roof models reconstructed from light detection and ranging (LiDAR) data, and contextual information of the scenes involved. The proposed method focuses on overcoming three types of common problems that can disturb the automatic roof extraction in the urban environment: perspective occlusions caused by high buildings, occlusions caused by vegetation covering the roof, and shadows that are adjacent to the roofs, which can be misinterpreted as roof edges. For this, an improved Snake-based mathematical model is developed considering the radiometric and geometric properties of roofs to represent the roof boundary in the image space. A new approach for calculating the corner response and a shadow compensation factor was added to the model. The created model is then adapted to represent the boundaries in the object space considering a stereo pair of aerial images. Finally, the optimal polyline, representing a selected roof boundary, is obtained by optimizing the proposed Snake-based model using a dynamic programming (DP) approach considering the contextual information of the scene. The results showed that the proposed method works properly in boundary extraction of roofs with occlusion and shadows areas, presenting completeness and correctness average values above 90%, RMSE average values below 0.5 m for E and N components, and below 1 m for H component.

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UAV building point cloud contour extraction based on the feature recognition of adjacent points distribution

Wu,

Chen,

Jin

et al. 2024

Measurement

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Extraction of building roof contours from the integration of high-resolution aerial imagery and laser data using Markov random fields

Cited by 4 publications

References 22 publications

Multi-channel recurrent attention network for building extraction from high resolution remote sensing images

Multi-channel recurrent attention network for building extraction from high resolution remote sensing images

Snake-Based Model for Automatic Roof Boundary Extraction in the Object Space Integrating a High-Resolution Aerial Images Stereo Pair and 3D Roof Models

UAV building point cloud contour extraction based on the feature recognition of adjacent points distribution

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