Demetre Argialas

Improving edge detection and watershed segmentation with anisotropic diffusion and morphological levellings

Edge preserving smoothing and image simplification is of fundamental importance in a variety of remote sensing applications during feature extraction and object detection procedures. The construction of a pre‐processing filtering tool for edge detection and segmentation tasks is still an open matter. Towards this end, this paper brings together two advanced nonlinear scale space representations, anisotropic diffusion filtering and morphological levellings, forming a processing scheme by their combination. The proposed scheme was applied to edge detection and watershed segmentation tasks. The experimental results showed that the developed scheme generated an effective pre‐processing tool for automatic olive tree detection and solving watershed over‐segmentation problems.

Automatic detection and tracking of oil spills in SAR imagery with level set segmentation

Automatic detection and monitoring of oil spills and illegal oil discharges is of fundamental importance in ensuring compliance with marine legislation and protection of the coastal environments, which are under considerable threat from intentional or accidental oil spills, uncontrolled sewage and wastewater discharged. In this paper, the level set based image segmentation was evaluated for the real‐time detection and tracking of oil spills from SAR imagery. The processing scheme developed consists of a pre‐processing step, in which an advanced image simplification takes place, followed by a geometric level set segmentation for the detection of possible oil spills. Finally, a classification was performed for the separation of look‐alikes, leading to oil spill extraction. Experimental results demonstrate that the level set segmentation is a robust tool for the detection of possible oil spills, copes well with abrupt shape deformations and splits and outperforms earlier efforts that were based on different types of thresholds or edge detection techniques. The developed algorithms efficiency for real‐time oil spill detection and monitoring was also tested.

A Fuzzy Spatial Reasoner for Multi-Scale GEOBIA Ontologies

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.

Geomorphological feature extraction from a digital elevation model through fuzzy knowledge-based classification

The objective of this research was the investigation of advanced image analysis methods for geomorphological mapping. Methods employed included multiresolution segmentation of the Digital Elevation Model (DEM) GTOPO30 and fuzzy knowledge based classification of the segmented DEM into three geomorphological classes: mountain ranges, piedmonts and basins. The study area was a segment of the Basin and Range Physiographic Province in Nevada, USA. The implementation was made in eCognition. In particular, the segmentation of GTOPO30 resulted into primitive objects. The knowledge-based classification of the primitive objects based on their elevation and shape parameters, resulted in the extraction of the geomorphological features. The resulted boundaries in comparison to those by previous studies were found satisfactory. It is concluded that geomorphological feature extraction can be carried out through fuzzy knowledge based classification as implemented in eCognition.

Evaluation of selected edge detection techniques in remotely sensing images

This paper describes the experience gained from the evaluation of selected automatic edge detection techniques applied to LANDSAT TM, SPOT HRV, IRS 1C and IKONOS images. Emphasis was given to the detection of man-made objects and linear features such as coastlines, roads and parcel boundaries in combination with selected preprocessing and postprocessing operations. As preprocessing Gaussian, adaptive and morphological operators were implemented and tested for image enhancement and smoothing. Edge extraction processing followed. First the Canny edge detector was applied. Then a morphological nonlinear Laplacian operator was applied and its zero-crossings yielded edge locations. Finally an edge detector resulting by overlaying two thresholded images from the Prewitt gradient, preserving edges appearing in both images, was applied. Postprocessing followed to eliminate noisy edges and restore edge connectivity through morphological operators. An analysis of the relative performance of the processing scheme indicated each detectors relation to noise (features at certain undesired scales, shadows along roads boundaries, irrelevant edges within parcel boundaries) and the set of specific parameters needed for proper enhancement and smoothing before edge extraction.

Building change detection through multi-scale GEOBIA approach by integrating deep belief networks with fuzzy ontologies

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%.

Integrating thermal and hyperspectral remote sensing for the detection of coastal springs and submarine groundwater discharges

This research focuses on the investigation of remote-sensing techniques for the detection of coastal sub-aerial springs and submarine groundwater discharges using airborne thermal and hyperspectral imagery. Very high spatial resolution thermal and hyperspectral images were acquired using Thermal Airborne Broadband Imager 320 (TABI-320) and Compact Airborne Spectrographic Imager 550 (CASI-550) sensors. Extensive in situ spectroradiometer and oceanographic measurements were carried out in parallel with thermal and hyperspectral image acquisitions. Experiments and analysis of the data show that the combined use of very high spatial resolution airborne thermal and hyperspectral sensors for the detection of relatively small sub-aerial coastal springs and submarine groundwater discharges proves to be a very efficient and operational method. Very high spatial resolution thermal data were able to detect even very small coastal sub-aerial springs. On the other hand, the hyperspectral data were the most appropriate for detecting relatively small submarine groundwater discharges, which were not detected on thermal imagery, due to the increase in turbidity that these discharges cause. This is confirmed by the strong correlations between the hyperspectral data and the in situ measured turbidity-related water inherent optical properties.

Implementation and evaluation of spatial filtering and edge detection techniques for lineament mapping: case study - Alevrada, Central Greece

The aim of the present work was to implement and evaluate spatial filtering and automatic edge extraction techniques for assisting the geological lineament detection process. The selected study area was Alevrada in Central Greece, an area of sedimentary terrain with many faults and folds. A Landsat-7 ETM+ image of the study area was geometrically registered on the geological map, and then radiometrically corrected, to subtract the path radiance of the optical bands. Various linear and nonlinear spatial high pass operators (Laplacian, Ford, directional filters, Sobel, Kirsch) were applied and an interpretation of the lineaments was made. Certain edge detection algorithms introduced in medical imaging and scene analysis were applied and assessed, including the Canny multi-scale edge detector, the Rothwell edge detector based on edge topology and the Blacks anisotropic diffusion edge detector, followed by morphological cleaning and pruning processes. The interpreted lineaments were qualitatively compared to the edge maps derived from the edge extraction algorithms, and a satisfactory matching was observed. This work provides a preliminary step towards lineament mapping automation.

Mapping urban green from IKONOS data by an object-oriented knowledge-base and fuzzy logic

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

Modeling the impact of large-scale transportation infrastructure development on land cover

Abstract Transportation infrastructure and urban development are tightly connected and interact with each other. The objective of this paper is to measure the impact of large-scale transportation infrastructure locations on land cover. Spatial econometric models that link the impact of transportation infrastructure onto the urban evolution have been used with panel data (multiple time points). Land cover information was derived individually from two Landsat TM images (1984 and 2010) by applying a semi-automatic object-based image analysis (OBIA) approach, resulting in six generalized thematic categories. Data from multiple databases have been collected and cross-referenced. The study area is the Athens metropolitan area, Greece, where, during the last few decades, crucial transportation infrastructure has been developed and significant urbanization has been observed. Moreover, a fixed effects model has been developed for a study area, providing additional information for the differences across municipalities. The results of this research provide evidence toward the quantification of the impact of major road transportation infrastructure in urban development and can have applications in other areas that have not been yet fully developed.