ISPRS International Journal of Geo-Information

Research

5860 KiB

Open AccessArticle

Extraction and Simplification of Building Façade Pieces from Mobile Laser Scanner Point Clouds for 3D Street View Services

by Yan Li, Qingwu Hu, Meng Wu, Jianming Liu and Xuan Wu

ISPRS Int. J. Geo-Inf. 2016, 5(12), 231; https://doi.org/10.3390/ijgi5120231 - 5 Dec 2016

Cited by 23 | Viewed by 6966

Abstract

Extraction and analysis of building façades are key processes in the three-dimensional (3D) building reconstruction and realistic geometrical modeling of the urban environment, which includes many applications, such as smart city management, autonomous navigation through the urban environment, fly-through rendering, 3D street view, [...] Read more.

Extraction and analysis of building façades are key processes in the three-dimensional (3D) building reconstruction and realistic geometrical modeling of the urban environment, which includes many applications, such as smart city management, autonomous navigation through the urban environment, fly-through rendering, 3D street view, virtual tourism, urban mission planning, etc. This paper proposes a building facade pieces extraction and simplification algorithm based on morphological filtering with point clouds obtained by a mobile laser scanner (MLS). First, this study presents a point cloud projection algorithm with high-accuracy orientation parameters from the position and orientation system (POS) of MLS that can convert large volumes of point cloud data to a raster image. Second, this study proposes a feature extraction approach based on morphological filtering with point cloud projection that can obtain building facade features in an image space. Third, this study designs an inverse transformation of point cloud projection to convert building facade features from an image space to a 3D space. A building facade feature with restricted facade plane detection algorithm is implemented to reconstruct façade pieces for street view service. The results of building facade extraction experiments with large volumes of point cloud from MLS show that the proposed approach is suitable for various types of building facade extraction. The geometric accuracy of building façades is 0.66 m in x direction, 0.64 in y direction and 0.55 m in the vertical direction, which is the same level as the space resolution (0.5 m) of the point cloud. Full article

(This article belongs to the Special Issue Mathematical Morphology in Geoinformatics)

► Show Figures

Figure 1

1257 KiB

Open AccessArticle

Retrieval of Remote Sensing Images with Pattern Spectra Descriptors

by Petra Bosilj, Erchan Aptoula, Sébastien Lefèvre and Ewa Kijak

ISPRS Int. J. Geo-Inf. 2016, 5(12), 228; https://doi.org/10.3390/ijgi5120228 - 2 Dec 2016

Cited by 47 | Viewed by 5369

Abstract

The rapidly increasing volume of visual Earth Observation data calls for effective content based image retrieval solutions, specifically tailored for their high spatial resolution and heterogeneous content. In this paper, we address this issue with a novel local implementation of the well-known morphological [...] Read more.

The rapidly increasing volume of visual Earth Observation data calls for effective content based image retrieval solutions, specifically tailored for their high spatial resolution and heterogeneous content. In this paper, we address this issue with a novel local implementation of the well-known morphological descriptors called pattern spectra. They are computationally efficient histogram-like structures describing the global distribution of arbitrarily defined attributes of connected image components. Besides employing pattern spectra for the first time in this context, our main contribution lies in their dense calculation, at a local scale, thus enabling their combination with sophisticated visual vocabulary strategies. The Merced Landuse/Landcover dataset has been used for comparing the proposed strategy against alternative global and local content description methods, where the introduced approach is shown to yield promising performances. Full article

(This article belongs to the Special Issue Mathematical Morphology in Geoinformatics)

► Show Figures

Figure 1

7755 KiB

Open AccessArticle

Morphological PDEs on Graphs for Image Processing on Surfaces and Point Clouds

by Abderrahim Elmoataz, François Lozes and Hugues Talbot

ISPRS Int. J. Geo-Inf. 2016, 5(11), 213; https://doi.org/10.3390/ijgi5110213 - 12 Nov 2016

Cited by 2 | Viewed by 5033

Abstract

Partial Differential Equations (PDEs)-based morphology offers a wide range of continuous operators to address various image processing problems. Most of these operators are formulated as Hamilton–Jacobi equations or curve evolution level set and morphological flows. In our previous works, we have proposed a [...] Read more.

Partial Differential Equations (PDEs)-based morphology offers a wide range of continuous operators to address various image processing problems. Most of these operators are formulated as Hamilton–Jacobi equations or curve evolution level set and morphological flows. In our previous works, we have proposed a simple method to solve PDEs on point clouds using the framework of PdEs (Partial difference Equations) on graphs. In this paper, we propose to apply a large class of morphological-based operators on graphs for processing raw 3D point clouds and extend their applications for the processing of colored point clouds of geo-informatics 3D data. Through illustrations, we show that this simple framework can be used in the resolution of many applications for geo-informatics purposes. Full article

(This article belongs to the Special Issue Mathematical Morphology in Geoinformatics)

► Show Figures

Figure 1

6301 KiB

Open AccessArticle

Granulometric Analysis on Remote Sensing Images: Application to Mapping Retrospective Changes in the Sahelian Ligneous Cover

by José Luis San Emeterio and Catherine Mering

ISPRS Int. J. Geo-Inf. 2016, 5(10), 192; https://doi.org/10.3390/ijgi5100192 - 13 Oct 2016

Cited by 2 | Viewed by 4211

Abstract

This paper illustrates how the use of mathematical morphology can be a powerful tool for the mapping of ligneous cover in semi-arid lands. Ligneous cover plays a fundamental role in Sahel semi-arid regions since this resource is vital to the resilience of rural [...] Read more.

This paper illustrates how the use of mathematical morphology can be a powerful tool for the mapping of ligneous cover in semi-arid lands. Ligneous cover plays a fundamental role in Sahel semi-arid regions since this resource is vital to the resilience of rural societies and can be used as an indicator of socio-environmental conditions. Grey tone vertical images from Sahelian villages in 1975 and 2010/2011 have been selected to perform a diachronic analysis to test the method. Granulometric profiles have been calculated for each pixel and then an unsupervised classification has been performed to obtain k classes that account for ligneous patches of different sizes. This method is particularly successful when the most recent images are used, given that these have better contrast and sharpness. Nested classifications were required to accomplish the ligneous mapping of images from 1975. The accuracy assessment for the most recent images classifications shows satisfactory results. The classification of ligneous cover according to different sizes is important for a better understanding of the ligneous dynamics. Full article

(This article belongs to the Special Issue Mathematical Morphology in Geoinformatics)

► Show Figures

Figure 1

6192 KiB

Open AccessArticle

Morphological Operations to Extract Urban Curbs in 3D MLS Point Clouds

by Borja Rodríguez-Cuenca, Silverio García-Cortés, Celestino Ordóñez and María C. Alonso

ISPRS Int. J. Geo-Inf. 2016, 5(6), 93; https://doi.org/10.3390/ijgi5060093 - 14 Jun 2016

Cited by 23 | Viewed by 5768

Abstract

Automatic curb detection is an important issue in road maintenance, three-dimensional (3D) urban modeling, and autonomous navigation fields. This paper is focused on the segmentation of curbs and street boundaries using a 3D point cloud captured by a mobile laser scanner (MLS) system. [...] Read more.

Automatic curb detection is an important issue in road maintenance, three-dimensional (3D) urban modeling, and autonomous navigation fields. This paper is focused on the segmentation of curbs and street boundaries using a 3D point cloud captured by a mobile laser scanner (MLS) system. Our method provides a solution based on the projection of the measured point cloud on the XY plane. Over that plane, a segmentation algorithm is carried out based on morphological operations to determine the location of street boundaries. In addition, a solution to extract curb edges based on the roughness of the point cloud is proposed. The proposed method is valid in both straight and curved road sections and applicable both to laser scanner and stereo vision 3D data due to the independence of its scanning geometry. The proposed method has been successfully tested with two datasets measured by different sensors. The first dataset corresponds to a point cloud measured by a TOPCON sensor in the Spanish town of Cudillero. The second dataset corresponds to a point cloud measured by a RIEGL sensor in the Austrian town of Horn. The extraction method provides completeness and correctness rates above 90% and quality values higher than 85% in both studied datasets. Full article

(This article belongs to the Special Issue Mathematical Morphology in Geoinformatics)

► Show Figures

Figure 1

8371 KiB

Open AccessArticle

Morphological Principal Component Analysis for Hyperspectral Image Analysis

by Gianni Franchi and Jesús Angulo

ISPRS Int. J. Geo-Inf. 2016, 5(6), 83; https://doi.org/10.3390/ijgi5060083 - 3 Jun 2016

Cited by 15 | Viewed by 12182

Abstract

This article deals with the issue of reducing the spectral dimension of a hyperspectral image using principal component analysis (PCA). To perform this dimensionality reduction, we propose the addition of spatial information in order to improve the features that are extracted. Several approaches [...] Read more.

This article deals with the issue of reducing the spectral dimension of a hyperspectral image using principal component analysis (PCA). To perform this dimensionality reduction, we propose the addition of spatial information in order to improve the features that are extracted. Several approaches proposed to add spatial information are discussed in this article. They are based on mathematical morphology operators. These morphological operators are the area opening/closing, granulometries and grey-scale distance function. We name the proposed family of techniques the Morphological Principal Component Analysis (MorphPCA). Present approaches provide new feature spaces able to jointly handle the spatial and spectral information of hyperspectral images. They are computationally simple since the key element is the computation of an empirical covariance matrix which integrates simultaneously both spatial and spectral information. The performance of the different feature spaces is assessed for different tasks in order to prove their practical interest. Full article

(This article belongs to the Special Issue Mathematical Morphology in Geoinformatics)

► Show Figures

Figure 1

18380 KiB

Open AccessArticle

An Efficient Parallel Algorithm for Multi-Scale Analysis of Connected Components in Gigapixel Images

by Michael H.F. Wilkinson, Martino Pesaresi and Georgios K. Ouzounis

ISPRS Int. J. Geo-Inf. 2016, 5(3), 22; https://doi.org/10.3390/ijgi5030022 - 25 Feb 2016

Cited by 7 | Viewed by 5346

Abstract

Differential Morphological Profiles (DMPs) and their generalized Differential Attribute Profiles (DAPs) are spatial signatures used in the classification of earth observation data. The Characteristic-Salience-Leveling (CSL) is a model allowing the compression and storage of the multi-scale information contained in the DMPs and DAPs [...] Read more.

Differential Morphological Profiles (DMPs) and their generalized Differential Attribute Profiles (DAPs) are spatial signatures used in the classification of earth observation data. The Characteristic-Salience-Leveling (CSL) is a model allowing the compression and storage of the multi-scale information contained in the DMPs and DAPs into raster data layers, used for further analytic purposes. Computing DMPs or DAPs is often constrained by the size of the input data and scene complexity. Addressing very high resolution remote sensing gigascale images, this paper presents a new concurrent algorithm based on the Max-Tree structure that allows the efficient computation of CSL. The algorithm extends the “one-pass” method for computation of DAPs, and delivers an attribute zone segmentation of the underlying trees. The DAP vector field and the set of multi-scale characteristics are computed separately and in a similar fashion to concurrent attribute filters. Experiments on test images of 3.48 to 3.96 Gpixel showed an average computational speed of 59.85 Mpixel per second, or 3.59 Gpixel per minute on a single 2U rack server with 64 opteron cores. The new algorithms could be extended to morphological keypoint detectors capable of handling gigascale images. Full article

(This article belongs to the Special Issue Mathematical Morphology in Geoinformatics)

► Show Figures

Figure 1

10448 KiB

Open AccessArticle

Segmentation of Façades from Urban 3D Point Clouds Using Geometrical and Morphological Attribute-Based Operators

by Andrés Serna, Beatriz Marcotegui and Jorge Hernández

ISPRS Int. J. Geo-Inf. 2016, 5(1), 6; https://doi.org/10.3390/ijgi5010006 - 19 Jan 2016

Cited by 23 | Viewed by 12746

Abstract

3D building segmentation is an important research issue in the remote sensing community with relevant applications to urban modeling, cloud-to-cloud and cloud-to-model registration, 3D cartography, virtual reality, cultural heritage documentation, among others. In this paper, we propose automatic, parametric and robust approaches to [...] Read more.

3D building segmentation is an important research issue in the remote sensing community with relevant applications to urban modeling, cloud-to-cloud and cloud-to-model registration, 3D cartography, virtual reality, cultural heritage documentation, among others. In this paper, we propose automatic, parametric and robust approaches to segment façades from 3D point clouds. Processing is carried out using elevation images and 3D decomposition, and the final result can be reprojected onto the 3D point cloud for visualization or evaluation purposes. Our methods are based on geometrical and geodesic constraints. Parameters are related to urban and architectural constraints. Thus, they can be set up to manage façades of any height, length and elongation. We propose two methods based on façade marker extraction and a third method without markers based on the maximal elongation image. This work is developed in the framework of TerraMobilita project. The performance of our methods is proved in our experiments on TerraMobilita databases using 2D and 3D ground truth annotations. Full article

(This article belongs to the Special Issue Mathematical Morphology in Geoinformatics)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Mathematical Morphology in Geoinformatics

Share This Special Issue

Special Issue Editor

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI