Special Issue "Mathematical Morphology in Geoinformatics"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 June 2016)

Special Issue Editor

Guest Editor
Dr. Beatriz Marcotegui

MINES ParisTech, CMM–Centre de Morphologie Mathematique, Mathematiques et Systemes, 35, rue St Honore, 77305-Fontainebleau-cedex, France
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Special Issue Information

Dear Colleagues,

Geoinformatics is the science dealing with the capture, analysis, interpretation, dissemination, and use of geographic information. Geographic information is extensively used in applications such as basic mapping, environmental management, transport and telecommunication or urban planning, agriculture management, climate change monitoring and many others.

During the last decades, numerous new evolved acquisition systems have been developed. They provide an ever increasing amount of extremely rich data: Lidar systems, either aerial or terrestrial, hyper-spectral images with an increasing number of channels and improved resolution, among others. Automatic or semi-automatic processing techniques are required in order to extract semantic information from this data, useful to develop practical applications.

Mathematical Morphology is a non-linear image processing technique, based on the set theory. It quantitatively describes image content in terms of shape and size, in an elegant mathematical framework. It has proven to be an extremely effective technique in many applications and in particular in geographic and geoinformatic applications.

This Special Issue aims at providing a cutting edge vision of mathematical morphology advances for geoinformatics applications. Experimental and theoretical results are expected.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Point cloud segmentation, classification.
  • Urban scene analysis and modeling.
  • Hyper-spectral image filtering, segmentation, classification
  • Land use cover
  • Change detection
  • Satellite-based geographical services: forestry monitoring, soil moisture, damage assessment, …

Authors from academia or industry working in the above or closely related research areas are encouraged to submit original manuscripts that have not been published and are not currently under review by other journals. Prospective authors should submit the complete manuscript through the online system (http://susy.mdpi.com/user/manuscripts/upload?journal=ijgi).

Dr Beatriz Marcotegui
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this Special Issue is 150 CHF (Swiss Francs).

 

Published Papers (8 papers)

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Research

Open AccessArticle Extraction and Simplification of Building Façade Pieces from Mobile Laser Scanner Point Clouds for 3D Street View Services
ISPRS Int. J. Geo-Inf. 2016, 5(12), 231; doi:10.3390/ijgi5120231
Received: 31 July 2016 / Revised: 24 November 2016 / Accepted: 26 November 2016 / Published: 5 December 2016
Cited by 2 | PDF Full-text (5860 KB) | HTML Full-text | XML Full-text
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)
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Open AccessArticle Retrieval of Remote Sensing Images with Pattern Spectra Descriptors
ISPRS Int. J. Geo-Inf. 2016, 5(12), 228; doi:10.3390/ijgi5120228
Received: 5 June 2016 / Revised: 11 November 2016 / Accepted: 17 November 2016 / Published: 2 December 2016
Cited by 2 | PDF Full-text (1257 KB) | HTML Full-text | XML Full-text
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)
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Open AccessArticle Morphological PDEs on Graphs for Image Processing on Surfaces and Point Clouds
ISPRS Int. J. Geo-Inf. 2016, 5(11), 213; doi:10.3390/ijgi5110213
Received: 30 June 2016 / Revised: 24 October 2016 / Accepted: 4 November 2016 / Published: 12 November 2016
PDF Full-text (7755 KB) | HTML Full-text | XML Full-text
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)
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Open AccessArticle Granulometric Analysis on Remote Sensing Images: Application to Mapping Retrospective Changes in the Sahelian Ligneous Cover
ISPRS Int. J. Geo-Inf. 2016, 5(10), 192; doi:10.3390/ijgi5100192
Received: 30 June 2016 / Revised: 26 September 2016 / Accepted: 30 September 2016 / Published: 13 October 2016
PDF Full-text (6301 KB) | HTML Full-text | XML Full-text
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)
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Figure 1

Open AccessArticle Morphological Operations to Extract Urban Curbs in 3D MLS Point Clouds
ISPRS Int. J. Geo-Inf. 2016, 5(6), 93; doi:10.3390/ijgi5060093
Received: 24 December 2015 / Revised: 9 May 2016 / Accepted: 9 May 2016 / Published: 14 June 2016
Cited by 4 | PDF Full-text (6192 KB) | HTML Full-text | XML Full-text
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)
Open AccessArticle Morphological Principal Component Analysis for Hyperspectral Image Analysis
ISPRS Int. J. Geo-Inf. 2016, 5(6), 83; doi:10.3390/ijgi5060083
Received: 17 December 2015 / Revised: 10 May 2016 / Accepted: 11 May 2016 / Published: 3 June 2016
Cited by 2 | PDF Full-text (8371 KB) | HTML Full-text | XML Full-text
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)
Open AccessArticle An Efficient Parallel Algorithm for Multi-Scale Analysis of Connected Components in Gigapixel Images
ISPRS Int. J. Geo-Inf. 2016, 5(3), 22; doi:10.3390/ijgi5030022
Received: 16 December 2015 / Revised: 28 January 2016 / Accepted: 3 February 2016 / Published: 25 February 2016
Cited by 1 | PDF Full-text (18380 KB) | HTML Full-text | XML Full-text
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)
Open AccessArticle Segmentation of Façades from Urban 3D Point Clouds Using Geometrical and Morphological Attribute-Based Operators
ISPRS Int. J. Geo-Inf. 2016, 5(1), 6; doi:10.3390/ijgi5010006
Received: 29 October 2015 / Revised: 4 December 2015 / Accepted: 14 December 2015 / Published: 19 January 2016
Cited by 3 | PDF Full-text (10448 KB) | HTML Full-text | XML Full-text
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)

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