Papers by Demetre Argialas
Aggregate material deposits which can be processed economically for construction materials are an... more Aggregate material deposits which can be processed economically for construction materials are an important resource for the state and the Louisiana Department of Transportation and Development (LADOTD). This need, together with the shortage of aggregates in the state, necessitated a thorough study in development of an exploration methodology for mapping the availability, location and extent of aggregate materials. This report describes a comprehensive exploration methodology which involves the integration of terrain analysis techniques, and geomorphological and geotechnical studies to locate aggregates in Louisiana. Landform analysis from aerial photographs along with geomorphic analysis to topographic forms has indicated the areas of promise which required futher in-situ investigations. Ground methods, employing both geotechnical and field sampling techniques, have aided stratigraphic analysis and refined the image based expectations. The techniques employed are explained in detail so that they can be readily put into practice. Specific sites were identified, one in each region of Louisiana, and are used as examples to demonstrate the principles of exploration from broad scale mapping to site-specific exploration. In summary, sand and gravel deposits are generally associated with modern river floodplains and valleyflanking Late Pleistocene terraces. In the coastal terraces of south Louisiana, gravel-bearing river trends also occur oblique to present river systems. Older gravel-bearing trends of modern and Late Pleistocene rivers are the most favorable for mining since cost-effective hydraulic mining techniques can be used. The older deposits provide suitable quantities of gravel by dry mining techniques when increased clay fractions and presence of iron oxides are not important considerations.
This chapter reports on a set of four developments, which have resulted in prototype computer sof... more This chapter reports on a set of four developments, which have resulted in prototype computer software systems related to terrain analysis and interpretation based on techniques from advanced image processing and machine vision. The first is the computational description and identification of drainage patterns through structural pattern recognition. The second uses a variety of expert system methods and tools to address terrain knowledge representation and to construct prototype expert systems for inferring both landform and the physiographic region of a site from user observations of indicators. The third concerns a “terrain visual vocabulary” based on a Macintosh hypermedia system consisting of interlinked definitions, graphics, and aerial images which can be used simultaneously with expert systems to assist novice interpreters. The fourth development concerns a geomorphometric approach for the classification of the GTOP030 digital elevation model into three classes of physiographic features and for the identification, representation, and classification of mountain objects.
Bulletin of the Geological Society of Greece, Jul 27, 2017
Proceedings of SPIE, Mar 14, 2003
ABSTRACT The objective of the present research effort was the investigation of expert system clas... more ABSTRACT The objective of the present research effort was the investigation of expert system classification techniques for land use mapping from very high resolution images for a typical Greek landscape. Data used included an IKONOS image of the Arkadi area in Crete acquired on September 2000, and a digital terrain model. Photointerpretation was carried out using color composites, band ratios and maps of scale 1:5.000 and 1:50.000. Maximum likelihood was used for per pixel supervised classification and its accuracy was 72%. A knowledge base containing 51 rules, 44 hypotheses and 12 variables was developed in the Expert Classifier module of ERDAS Imagine. A hierarchical organization of thematic classes was developed in four levels through photointerpretation and study of the spectral reflectance diagrams and thematic class histograms. The image was first classified into three general categories: water-like, vegetation-like and soil-like materials. These were then separated into sub-classes. Classification rules were enriched with ancillary data such as the slopes, the road network, the NDVI vegetation index, the results of a spatial model computing texture, and indices reflecting the polygon shape and perimeter. Overall accuracy of the classification with the expert system was 82%.
Proceedings of SPIE, Mar 14, 2003
Urban green is recognized as an important functional element of the city, which affects directly ... more Urban green is recognized as an important functional element of the city, which affects directly the standard of living. The present paper is concerned with the study of urban green by means of object-oriented image analysis of high-resolution IKONOS data. More specifically, the potential for detecting urban green and quantitatively assessing it was explored. The analysis included two levels of segmentation and classification. On the first level, objects to which the image was segmented were subsequently classified according to a vegetation index (Scaled MSAVI) to areas with dense, thin or no vegetation. On the second level the image was classified in larger areas that simulated building blocks according to the relative area of vegetation, in order to create a thematic map of urban green density. The evaluation of the results indicated that detection and quantitative assessment of urban green was achieved with satisfactory accuracy. The use of additional data (DEM, hyperspectral, GIS) will allow a more detail study of the urban green from high resolution data by means of object-oriented image analysis
Proceedings of SPIE, Mar 14, 2003
Photogrammetric Engineering and Remote Sensing, Jun 1, 2015
Abstract In Geographic Object-Based Image Analysis (GEOBIA) an image is partitioned into objects ... more Abstract In Geographic Object-Based Image Analysis (GEOBIA) an image is partitioned into objects by a segmentation algorithm. These objects are then classified into semantic categories based on unsupervised/ supervised methods, or knowledge-based methods, such as an ontology. The aim of this paper was to develop a SPatial Ontology Reasoner (SPOR) to allow the development of GEOBIA ontologies by employing fuzzy, spatial, and multi-scale representations, with time efficiency. An enhanced version of the Web Ontology Language 2 (OWL 2) with fuzzy representations was adopted and expanded to represent fuzzy spatial relationships within the framework of GEOBIA. Segmentation results are stored within PostgreSQL. An ontology described the class/subclass hierarchy and class definitions. SPOR integrated PostgreSQL and the ontology, to classify the objects. To demonstrate the framework, a QuickBird image was employed for building extraction. Accuracy assessment indicated that 87 percent of building rooftops were detected.
Bulletin of the Geological Society of Greece, Jul 27, 2017
Πανελλήνια και Διεθνή Γεωγραφικά Συνέδρια, Συλλογή Πρακτικών, 2010
Πηελ παξνχζα εξγαζία κειεηάηαη ε θαηάζηαζε stress ηεο Θεθαιιεληαθήο ειάηεο (Abies cephalonica), ζ... more Πηελ παξνχζα εξγαζία κειεηάηαη ε θαηάζηαζε stress ηεο Θεθαιιεληαθήο ειάηεο (Abies cephalonica), ζηηο 20 κφληκεο δνθηκαζηηθέο επηθάλεηεο ηνπ Δζληθνχ Γξπκνχ Ξάξλεζαο, ηνπ Λ. Αηηηθήο. Ζ γεληθή εηθφλα ηνπ ειαηνδάζνπο δελ είλαη θαιή, είλαη γεγνλφο φκσο φηη ε Θεθαιιεληαθή ειάηε παξά ηελ έληνλε θαθνκεηαρείξηζή ηεο θαηά ην παξειζφλ, θαηφξζσζε λα δηαηεξεζεί επί αηψλεο ζηελ Ξάξλεζα θαη λα αλαγελλάηε θπζηθά. Ιακβάλνληαο ππφςε φηη ην θιηκαηηθφ θαη εδαθηθφ πεξηβάιινλ ηεο Ξάξλεζαο δελ είλαη πνιχ θαηάιιειν γηα ηελ αλάπηπμή ηεο (ρακειφ πςφκεηξν, κεγάιε μεξαζία θαη πςειέο ζεξκνθξαζίεο ην θαινθαίξη, θησρά θαη αβαζή εδάθε), απνηειεί πιένλ είδνο κνλαδηθφ, αλαγθαίν θαη αλαληηθαηάζηαην γηα ηελ πεξηνρή, πνπ πξέπεη λα δηαηεξεζεί. Πθνπφο ηεο κειέηεο είλαη ε πηινηηθή δηεξεχλεζε ηεο θαηάζηαζεο stress ησλ ειάησλ ηνπ δξπκνχ αλά βαζκίδα πξνζβνιήο βάζεη ηεο νδεγίαο ηεο ΔΝΘ (Δ.Ν.Θ / 1984-Γηάγλσζε θαη θαηάζηαζε δεκηψλ ζηα δάζε‖: έληνλν stress: Η, ιηγφηεξν έληνλν stress: ΗΗ, αζζελέο stress: ΗΗΗ) ρξεζηκνπνηψληαο δχν πεξηβαιινληηθνχο δείθηεο, ηνλ θαλνληθνπνηεκέλν δείθηε βιάζηεζεο (NDVI) κέζσ ηεο αληηθεηκελνζηξαθνχο αλάιπζεο ηεο εηθφλαο, ε νπνία απνηειεί πξνρσξεκέλε κέζνδν ηειεπηζθφπεζεο ζε ζπλδπαζκφ κε ηελ in situ παξαηήξεζε θαη ηελ κέηξεζε ηνπ δείθηε θζνξηζκνχ (fluorescence) κέζσ ηνπ θαζκαηηθνχ νξγάλνπ PEA (Plant Efficiency Analyser). Ν ζπλδπαζκφο ηεο ηειεπηζθφπεζεο θαη ηεο in situ παξαηήξεζεο έδσζαλ αξθεηά ηθαλνπνηεηηθά απνηειέζκαηα ψζηε λα επεθηαζεί πέξαλ ηνπ πηινηηθνχ ζηαδίνπ ησλ ειάησλ πνπ παξνπζηάδνπλ stress (έληνλν stress: Η, ιηγφηεξν έληνλν stress: ΗΗ, αζζελέο stress: ΗΗΗ) ζ' φιε ηελ έθηαζε ηνπ ειαηνδάζνπο ή ζε παξφκνην δαζνπεξηβάιινλ, ρξεζηκνπνηψληαο ακθφηεξνπο ηνπο πεξηβαιινληηθνχο δείθηεο, γηα ηελ θαιχηεξε δηαρείξηζε θαη πξνζηαζία ηνπ Δζληθνχ Γξπκνχ Ξάξλεζαο.
Geocarto International, Sep 12, 2018
The aim of this study is to investigate the potential of Sentinel-2 imagery for the identificatio... more The aim of this study is to investigate the potential of Sentinel-2 imagery for the identification and determination of forest patches of particular interest, with respect to ecosystem integrity and biodiversity and to produce a relevant biodiversity map, based on Simpson's Diversity Index in Taxiarchis university research forest, Chalkidiki, North Greece. The research is based on OBIA being developed on to bi-temporal summer and winter Sentinel-2 imagery. Fuzzy rules, which are based on topographic factors, such as terrain elevation and slope for the distribution of each tree species, derived from expert knowledge and field observations, were used to improve the accuracy of tree species classification. Finally, Simpson's Diversity Index for forest tree species, was calculated and mapped, constituting a relative indicator for biodiversity for forest ecosystem organisms (fungi, insects, birds, reptiles, mammals) and carrying implications for the identification of patches prone to disturbance or that should be prioritised for conservation.
Survey Review, Nov 1, 2013
Abstract The aim of this paper was to investigate the development of a fuzzy knowledge base withi... more Abstract The aim of this paper was to investigate the development of a fuzzy knowledge base within an object based image analysis (OBIA) system for automatic change detection of buildings. A multitemporal analysis of very high resolution satellite data (QuickBird and IKONOS) was performed. Two case studies for the Keratea suburb of Athens, Greece were selected. For each dataset, primitive image objects were created through multi-resolution segmentation, in five hierarchical levels, following a mixed top-down strategy established by a trial-and-error procedure. Subsequently each object was assigned by fuzzy classification to one of the classes representing the land cover/use categories of each level. The aim of the classification procedure was to separate the image objects into buildings and not buildings, extract the changes occurring between the two dates, and perform qualitative and quantitative evaluation.
Photogrammetric Engineering and Remote Sensing, Feb 8, 2000
ABSTRACT
International Journal of Remote Sensing, 2002
A methodology was developed previously by the authors for the segmentation of the Global Digital ... more A methodology was developed previously by the authors for the segmentation of the Global Digital Elevation Model (GTOPO30) to three terrain classes (mountains, basins and piedmont slopes) and it was applied to the Great Basin Section (south-west USA). In the present research effort, mountain objects were identified through a connected component-labelling algorithm applied on the mountain terrain class. Taking into account the physical and perceptual attributes of the Great Basin mountain features, 12 morphometric attributes were defined for the mountain objects and were used as descriptors in their parametric representation. Finally, classification of mountain objects through the implementation of a K -means clustering algorithm resulted in four clusters of mountain objects that appeared to be spatially arranged to distinct geographic regions. The results were compared with existing maps and they were found to be in accordance with existing physiographic descriptions. It is concluded that the derived parametric representation of mountain objects carried sufficient physiographic information and it can be used for mountain classification. The conclusions point out the physiographic information content of GTOPO30 and its value and applications to regional geology and space geomorphology.
Computers & Geosciences, Aug 1, 1999
A methodology is presented for the segmentation of certain physiographic features (mountains, bas... more A methodology is presented for the segmentation of certain physiographic features (mountains, basins and piedmont slopes) observed in the Great Basin Section that belongs to the Basin and Range Physiographic Province of southwest USA, from the GTOPO30 global digital elevation model. A region-growing segmentation algorithm using as seeds ridge and valley pixels and appropriate gradient-region growing criteria, was applied for
CRC Press eBooks, Feb 26, 2001
ABSTRACT
Πανελλήνια και Διεθνή Γεωγραφικά Συνέδρια, Συλλογή Πρακτικών, 2010
The edge detection problem of man made objects has traditionally been addressed with the use of t... more The edge detection problem of man made objects has traditionally been addressed with the use of the Canny and Hough transforms. In recent years considerable interest was developed in new transforms that address the problem of edge detection, especially in case of high resolution satellite images. They are based on the wavelet transform. The challenge is to choose the appropriate wavelet for a particular application which is not known a priori. In this paper, two methods based on wavelets have been introduced and implemented for extracting edges. The first method was based on the biorthogonal mother wavelet while the second method was based on a new wavelet named "contourlet". Contourlets have been appropriately designed to locate the edges of an image, thus having a significant advantage compared to classical wavelets for edge detection. The evaluation was made using high resolution images containing specific features with a variety of geometric shapes, in order to understand better the advantages of the "new wavelet" transform. The results have shown that the wavelet transform using the biorthogonal wavelet produced accurate edge detection results on high resolution satellite images of urban areas Moreover, the contourlet gave very good results, in detecting roads, some of their types, and other linear features.
Photogrammetric Engineering and Remote Sensing, Jul 16, 1988
A structural pattern recognition methodology is presented for computer-assisted description and c... more A structural pattern recognition methodology is presented for computer-assisted description and classification of drainage patterns. Eight patterns types were quantitatively described and classified. The methodology to classify these patterns consisted of drainage pattern models, hierarchical and relational models, attribute extraction, and classification strategies. The results indicated that a digital computer can assist human interpretation of such patterns. TABLE 1. SIGNIFICANCE OF DRAINAGE PATTERNS.
International Journal of Image and Data Fusion, Mar 14, 2016
ABSTRACT Monitoring and mapping urban changes is of great importance for the development, plannin... more ABSTRACT Monitoring and mapping urban changes is of great importance for the development, planning and management of the urban zone, especially in countries with a rapidly growing urban area. The aim of this paper was to develop a GEographic Object-Based Image Analysis (GEOBIA) approach, by integrating Deep Learning classification and Fuzzy Ontologies through multi-scale analysis, to monitor building changes in suburban areas of Greece. Three suburban areas of east Attica, Greece were selected as representative to test the methodology. For each area, one QuickBird and one WorldView 2 image, taken in 2006 and 2011, respectively, were employed. Three segmentation levels and a three-level class hierarchy were developed for the extraction process. Deep Belief Networks were employed on the lowest level of the segmentation hierarchy (Level 1) for an initial detection of areas of possible change. To detect the changes in building infrastructure, the classification result of Level 1 was refined based on interpretation rules, developed on the upper levels of the hierarchy (Level 2 and Level 3). Accuracy assessment indicated that 93.5% of the total number of changes were successfully detected, while the commission error was less than 20%.
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Papers by Demetre Argialas