Roof planes detection via a second-order variational model

Benciolini, Battista; Ruggiero, Valeria; Vitti, Alfonso; Zanetti, Massimo

doi:10.1016/j.isprsjprs.2018.01.022

Cited by 10 publications

(5 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Owen [27] proposed a second order variational model to overcome limitations of the Mumford-Shah model. Solutions produced by the Blake-Zisserman model are basically nearly-linear piece-wise approximations of the data [22,[28][29][30][31][32].…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis

Case

Vitti

2021

IJGI

Self Cite

View full text Add to dashboard Cite

Digital images, and in particular satellite images acquired by different sensors, may present defects due to many causes. Since 2013, the Landsat 7 mission has been affected by a well-known issue related to the malfunctioning of the Scan Line Corrector producing very characteristic strips of missing data in the imagery bands. Within the vast and interdisciplinary image reconstruction application field, many works have been presented in the last few decades to tackle the specific Landsat 7 gap-filling problem. This work proposes another contribution in this field presenting an original procedure based on a variational image segmentation model coupled with radiometric analysis to reconstruct damaged images acquired in a multi-temporal scenario, typical in satellite remote sensing. The key idea is to exploit some specific features of the Mumford–Shah variational model for image segmentation in order to ease the detection of homogeneous regions which will then be used to form a set of coherent data necessary for the radiometric reconstruction of damaged regions. Two reconstruction approaches are presented and applied to SLC-off Landsat 7 data. One approach is based on the well-known histogram matching transformation, the other approach is based on eigendecomposition of the bands covariance matrix and on the sampling from Gaussian distributions. The performance of the procedure is assessed by application to artificially damaged images for self-validation testing. Both of the proposed reconstruction approaches had led to remarkable results. An application to very high resolution WorldView-3 data shows how the procedure based on variational segmentation allows an effective reconstruction of images presenting a great level of geometric complexity.

show abstract

Section: Discussionmentioning

confidence: 99%

Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis

Case

Vitti

2021

IJGI

Self Cite

View full text Add to dashboard Cite

show abstract

“…The extraction and identification of 3D urban buildings have become a crucial issue in many applications, such as urban building database updating, city management, disaster assess-The associate editor coordinating the review of this manuscript and approving it for publication was Yong Yang . ment, digital mapping, transportation planning, cadastral, and telecommunication network management [1], [2]. However, extracting 3D building information through field surveys is labor-intensive and time-consuming, yet usually unavoidable.…”

Section: Introductionmentioning

confidence: 99%

3D Urban Buildings Extraction Based on Airborne LiDAR and Photogrammetric Point Cloud Fusion According to U-Net Deep Learning Model Segmentation

Zhang

Wang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

The LiDAR and photogrammetric point clouds fusion procedure for building extraction according to U-Net deep learning model segmentation is provided and tested. Firstly, an initial geolocalization process is performed for photogrammetric point clouds generated using structure-from-motion and dense-matching methods. Then, point cloud segmentation is carried out based on U-Net deep learning model. The precision of the U-Net model for buildings extraction reachs 87%, with F-score of 0.89 and IoU of 0.80. It is shown that the U-Net method is effective for high-resolution image extraction. The detailed information can accurately be identified and extracted, such as vegetation located between buildings and roads. After segmentation, each chunk of the LiDAR and photogrammetric point clouds are finely registered and merged based on the iterative closest point algorithm. Finally, the fused point clouds are obtained. It shows that the structure and shape of the buildings could be delineated from the fused point clouds when both enough ground points and a higher point density are available. Furthermore, color information improves both visualization effect and properties identification. The experiments are conducted to extract individual buildings from three types of point clouds in three plots. A DoN (Difference of Normals) approach is used to isolate 3D buildings from other objects in densely built-up areas. It shows that most building extraction results have a Precision > 0.9 and favorable Recall and F-score values. Although the LiDAR extraction results have some advantages over the photogrammetric and fused ones in terms of Precision, the Recall and F-score results appear best for the fused point clouds. It shows that the fused data contains a high point density and RGB color information and could improve the building extraction.

show abstract

“…The identification and extraction of buildings have become crucial issues in many applications, such as urban basic geodatabase updating, city planning management, disaster assessment, digital mapping, transportation planning, cadastral management, acoustic and energy studies, and telecommunication network design [1][2][3][4]. Collecting building information by field survey is labor-intensive and timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds

et al. 2021

View full text Add to dashboard Cite

Point clouds derived from LiDAR (Light Detection and Ranging) and photogrammetry systems are used to extract building footprints in dense urban areas. Two extraction methods based on DSM (Digital Surface Model) images and point clouds are comprehensively evaluated and compared. Firstly, photogrammetric point clouds are generated from aerial images of downtown Guangzhou, China, and compared with corresponding LiDAR point clouds. Then, DSM images are created using these point clouds and a threshold segmentation method is applied for building extraction. Although regularized buildings can be extracted according to the selection of appropriate height thresholds for the LiDAR DSM and photogrammetric DSM, blurry building boundaries exist for results of photogrammetric DSM when high trees are available nearby. LiDAR DSM extraction performs better in terms of Precision, Recall, and F-score metrics. A DoN (Difference of Normals) approach based on point cloud datasets is also quantitatively and qualitatively demonstrated. Our experiments show that when a suitable radius threshold is selected, the method provides satisfactorily normal calculation results and can successfully isolate building roofs from other objects in densely built-up areas. The majority of building extraction results have a precision > 0.9 and favorable Recall and F-score results. There is high consistency between photogrammetric and LiDAR point clouds. Although LiDAR provides higher extraction accuracy, photogrammetry is also useful for its more convenient acquisition and higher point cloud densities.

show abstract

Roof planes detection via a second-order variational model

Cited by 10 publications

References 59 publications

Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis

Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis

3D Urban Buildings Extraction Based on Airborne LiDAR and Photogrammetric Point Cloud Fusion According to U-Net Deep Learning Model Segmentation

Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds

Contact Info

Product

Resources

About