Segmentation

There is a need to automate terrain feature mapping so that to make the process more objective and less time consuming by using proper feature extraction techniques. The objective of this study was the use of object-oriented image analysis methods for the automatic extraction of alluvial fan terrain units. The study area was located in the Death Valley, Nevada, USA. The data used included an ASTER L1 satellite image and the 1 o Digital Elevation Model. The methodology developed for alluvial fan extraction included preprocessing of the digital data: filtering of the Digital Elevation Model (DEM) for noise removal, a Fourier Transform Wedge filter for the elimination of striping in the ASTER data and geometric co-registration of the satellite and DEM data. A multiresolution segmentation technique was then developed, delivering object primitives at four resolution levels. At the first and finest level, three physiographic feature types (basins, piedmonts and mountains) were extracted from the DEM to be used in the rule-based fuzzy classification of the following levels. Then, a knowledge base including definitions of Alluvial materials, Mountains, Basin floor salt deposits and Basin floor sediments was implemented. The second level was classified by the nearest neighbour classifier using spectral information for the first iteration of the classification procedure. For a second iteration, the knowledge base was further expanded primarily with heuristics concerning contextual information of the alluvial materials related to the geomorphological features extracted at the first level. Finally, in the last level, a projection was made, classifying the image into two classes: Alluvial Fans and Not Alluvial fans. The method gave good results in detecting alluvial fan units, working best for large shape alluvial fans. Some minor problems were encountered for the smaller alluvial fans, due to the difficulty of their boundar

AN OBJECT BASED IMAGE ANALYSIS APPROACH FOR THE EXTRACTION OF THE KOLOUMBO VOLCANO AND ASSOCIATED DOMES-CONES FROM A DIGITAL SEABED ELEVATION MODEL

Η παρούσα μελέτη, αφορά στη μελέτη του θαλάσσιου πυθμένα από ψηφιακά μοντέλα αναγλύφου, με την ανάπτυξη μεθοδολογίας αντικειμενοστρεφούς ανάλυσης εικόνας. Έχει ως στόχο την αυτοματοποιημένη εξαγωγή γεωμορφολογικών χαρακτηριστικών πυθμένα, στον οποίο εντοπίζεται έντονη ηφαιστειακή δραστηριότητα. Η περιοχή μελέτης βρίσκεται στη λεκάνη της Ανύδρου, όπου δεσπόζει το υποθαλάσσιο ηφαίστειο του Κολούμπο, καθώς και μικρότεροι υποθαλάσσιοι ηφαιστειακοί κώνοι, 7 χλμ βορειοανατολικά της Σαντορίνης. Για το σκοπό αυτό, έγινε χρήση ψηφιακού μοντέλου αναγλύφου πυθμένα χωρικής ανάλυσης 50m και των παραγώγων αυτού: Slope, Topographic Position Index (TPI) και Terrain Ruggedness Index (TRI). Δημιουργήθηκαν συνολικά εννέα επίπεδα κατάτμησης και ταξινόμησης με στόχο την παραγωγή του τελικού επιπέδου κατάτμησης “level 5”, στο οποίο και ταξινομήθηκαν οι τελικές κατηγορίες γεωμορφολογικών χαρακτηριστικών. Τα αποτελέσματα της μεθόδου αξιολογήθηκαν με τη χρήση 1617 αλγορίθμων που αφορούν την ευστάθεια της ταξινόμησης, αλλά και με ποιοτική και ποσοτική σύγκριση των αποτελεσμάτων με υπάρχων χαρτογραφικό υλικό.This paper concerns the study of the seafloor through digital seabed elevation models, using object based image analysis methods. The goal of this research was the automated extraction of geomorphological features from the seabed in regions presenting intense volcanic activity. The study area is located around the submarine volcano of the Kolοumbo (in the submarine area northeast of the Santorini island, Greece). For this purpose, a Digital Elevation Model (DEM) of the seabed with a spatial resolution of 50m was used. Derivatives of the DEM, such us Slope, Topographic Position Index (TPI) and Terrain Ruggedness Index (TRI) were created in the open source software "QGIS 2.4". The implementation of the object based image analysis approach was performed in eCognition 8.7 software. Nine segmentation and classification levels were created in order to produce the final level segmentation "level 5", where the final geomorphological features were classified. The results of the method were evaluated using classification stability measures and qualitative and quantitative comparison of the results with existing map

A Region Merging Segmentation with Local Scale Parameters: Applications to Spectral and Elevation Data

Region merging is the most effective method for the segmentation of remote sensing data. The quality and the size of the resulted image objects is controlled by a global heterogeneity threshold, termed as the scale parameter. However, the multidimensional nature of the visible features in a scene defies the use of an even optimum single global scale parameter. In this study, a novel region merging segmentation method is proposed, where a local scale parameter is defined for each image object by its internal and external heterogeneity measures (i.e., local variance and Moran’s I). This method allows image objects with low internal and external heterogeneity to be further merged with higher scale parameter values, since they are more likely to be a part of an adjacent object, than objects with high internal and external heterogeneity. The proposed method was applied in spectral and elevation data and its results were evaluated visually and with supervised and unsupervised evaluation methods. The comparison with multi-resolution segmentation (MRS) showed that the proposed region merging method can produce improved segmentation results in terms of maximizing intra-object homogeneity and inter-object heterogeneity as well as in the delimitation of specific target objects, present in spectral and elevation data. The unsupervised evaluation results of the (1) Côte d’Azur, (2) Manchester, and (3) Szada images from the SZTAKI-INRIA building detection dataset showed that the proposed method (overall goodness, OGf (1): 0.7375, (2): 0.7923, (3): 0.7967) performs better than MRS (OGf (1): 0.7224, (2): 0.7648, (3): 0.7823). The higher values of OGf indicate their ability to produce segmentation results with reduced over-segmentation effects and without the need of presegmented input data, in contrast to the objective heterogeneity and relative homogeneity (OHRH) hybrid segmentation method (OGf (1): 0.5864, (2): 0.5151, (3): 0.6983)