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

Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region

The unpredictable climate in wet tropical regions along with the spatial resolution limitations of some satellite imageries make detecting and mapping artisanal and small-scale mining (ASM) challenging. The objective of this study was to test the utility of Pleiades and SPOT imagery with an object-based support vector machine (OB-SVM) classifier for the multi-temporal remote sensing of ASM and other land cover including a large-scale mine in the Didipio catchment in the Philippines. Historical spatial data on location and type of ASM mines were collected from the field and were utilized as training data for the OB-SVM classifier. The classification had an overall accuracy between 87% and 89% for the three different images—Pleiades-1A for the 2013 and 2014 images and SPOT-6 for the 2016 image. The main land use features, particularly the Didipio large-scale mine, were well identified by the OB-SVM classifier, however there were greater commission errors for the mapping of small-scale mines. The lack of consistency in their shape and their small area relative to pixel sizes meant they were often not distinguished from other land clearance types (i.e., open land). To accurately estimate the total area of each land cover class, we calculated bias-adjusted surface areas based on misclassification values. The analysis showed an increase in small-scale mining areas from 91,000 m2—or 0.2% of the total catchment area—in March 2013 to 121,000 m2—or 0.3%—in May 2014, and then a decrease to 39,000 m2—or 0.1%—in January 2016

DIGITAL EARTH OBSERVATION INFRASTRUCTURES AND INITIATIVES: A REVIEW FRAMEWORK BASED ON OPEN PRINCIPLES

Recent years have seen a tremendous increase of digital Earth Observation (EO) infrastructures, which provide web-based environments for accessing and processing data in a highly automated and scalable way. However, the current landscape of EO infrastructures and initiatives is fragmented, with various levels of user on-boarding and uptake success. The current work aims to make sense of this complex landscape by providing two main contributions. First, it offers a classification scheme used to review and analyse more than 150 EO infrastructures and initiatives. Then, adopting a user-centric perspective, the main limitations and obstacles currently faced by users when working with the existing EO platforms are identified. For each of these limitations, we propose a number of good practices that could benefit, from a user point of view, the design and functioning of EO platforms. Some technological enablers, i.e. specific resources (such as software components, standards and data encodings) that emerged from the analysis as holding a great potential for improving the usability of existing EO platforms, are finally listed. The work aims to provide a first scientific insight on how to best design and operate EO platforms to maximise the benefits of their user communities

Attention Reshapes Center-Surround Receptive Field Structure in Macaque Cortical Area MT

Directing spatial attention to a location inside the classical receptive field (cRF) of a neuron in macaque medial temporal area (MT) shifts the center of the cRF toward the attended location. Here we investigate the influence of spatial attention on the profile of the inhibitory surround present in many MT neurons. Two monkeys attended to the fixation point or to 1 of 2 random dot patterns (RDPs) placed inside or next to the cRF, whereas a third RDP (the probe) was briefly presented in quick succession across the cRF and surround. The probe presentation responses were used to compute a map of the excitatory receptive field and its inhibitory surround. Attention systematically reshapes the receptive field profile, independently shifting both center and surround toward the attended location. Furthermore, cRF size is changed as a function of relative distance to the attentional focus: attention inside the cRF shrinks it, whereas directing attention next to the cRF expands it. In addition, we find systematic changes in surround inhibition and cRF amplitude. This nonmultiplicative push–pull modulation of the receptive field's center-surround structure optimizes processing at and near the attentional focus to strengthen the representation of the attended stimulus while reducing influences from distractors

Assessing contextual descriptive features for plot-based classification of urban areas

A methodology for mapping urban land-use types integrating information from multiple data sources (high spatial resolution imagery, LiDAR data, and cadastral plots) is presented. A large set of complementary descriptive features that allow distinguishing different urban structures (historical, urban, residential, and industrial) is extracted and, after a selection process, a plot-based image classification approach applied, facilitating to directly relate the classification results and the urban descriptive parameters computed to the existent land-use/land-cover units in geospatial databases. The descriptive features are extracted by considering different hierarchical scale levels with semantic meaning in urban environments: buildings, plots, and urban blocks. Plots are characterised by means of image-based (spectral and textural), three-dimensional, and geometrical features. In addition, two groups of contextual features are defined: internal and external. Internal contextual features describe the main land cover types inside the plot (buildings and vegetation). External contextual features describe each object in terms of the properties of the urban block to which it belongs. After the evaluation in an heterogeneous Mediterranean urban area, the land-use classification accuracy values obtained show that the complementary descriptive features proposed improve the characterisation of urban typologies. A progressive introduction of the different groups of descriptive features in the classification tests show how the subsequent addition of internal and external contextual features have a positive effect by increasing the final accuracy of the urban classes considered in this study. © 2012 Elsevier B.V.The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation and FEDER in the framework of the projects CGL2009-14220 and CGL2010-19591/BTE, and the support of the Spanish Instituto Geografico Nacional (IGN).Hermosilla, T.; Ruiz Fernández, LÁ.; Recio Recio, JA.; Cambra López, M. (2012). Assessing contextual descriptive features for plot-based classification of urban areas. Landscape and Urban Planning. 106(1):124-137. doi:10.1016/j.landurbplan.2012.02.008S124137106

Investigation of image segmentation, machine learning and knowledge-based expert system methods in remote sensing

The objective of this research was to research and implement state-of-the-art computer vision and machine learning methods for Object-Based Image Analysis (OBIA), as well as integration with knowledge-based expert systems. The first contribution was the development of a generic image segmentation algorithm, as a low level processing part of an integrated object-oriented image analysis system. The implemented algorithm is called MSEG and can be described as a region merging procedure. The second contribution of this research involved the design and development of a region-based multi-scale segmentation algorithm with the integration of complex texture features. The third contribution of this research involved the integration of Support Vector Machines (SVM) with OBIA. The Support Vector Machine is a theoretically superior machine learning methodology with great results in pattern recognition. Especially for supervised classification of high-dimensional datasets and has been found competitive with the best machine learning algorithms. In the past, SVMs were tested and evaluated only as pixel-based image classifiers. In the forth contribution of this research, an object-oriented image classification framework was developed which incorporates nonlinear scale space filtering into the multi-scale segmentation and classification procedures. Morphological levelings, which possess a number of desired spatial and spectral properties, were associated with anisotropically diffused markers towards the construction of nonlinear scale spaces. The fifth contribution of this research involved the development of a multiscale object-oriented image analysis framework, which incorporated a region merging segmentation algorithm enchanced by advanced edge features and nonlinear scale space filtering. The sixth contribution of this research involved the implementation of an object-based image classification method, incorporating the Relevance Vector Machine framework for image object classification. The Relevance Vector Machine (RVM) is a kernel classification method, extending the Support Vector Machine (SVM) through Bayessian theory, dealing with uncertainty within the kernel-based classification framework. Finally, in the seventh contribution of this research, a multimodal object-based image classification approach was developed and evaluated. The goal of this research was to integrate machine learning classification with knowledge-based expert systems, to extend the Object-Based Image Analysis methodology and to evaluate its effectiveness and prospects. The previously developed framework, based on scale-space representation and a state-of-the-art multi-scale image segmentation algorithm provided the primitive image objects, while their spatial and spectral properties formed a multidimensional feature space for the following classification steps. Then, a sparse kernel-based supervised classification method was developed, based on Relevance Vector Machines, which provided the initial spectral classification of the image primitives. This sparse classifier was able to perform with high accuracy even with a small number of sample objects while maintaining the accuracy performance of SVM methods. The classified image objects and their spatial and spectral features were represented and stored in a spatial database, using a vector representation in order to perform spatial analysis and reduce the memory footprint of the system. Then, a knowledge-based expert system was integrated into the spatial database to provide rule-based classification support through a pure expert system tool: CLIPS. This approach was evaluated against other object-based classification methods and some very promising experimental results were demonstrated. Overall, this disertation contributed in all parts of the Object-Based Image Analysis methodology, introducing and implementing state-of-the-art computer vision, image segmentation, machine learning and knowledge-based methods in the OBIA methodology, thus integrating new tools towards automating the classification and feature extraction tasks in remote sensing. This need for more accurate and automated classification is crucial in remote sensing today, since photointerpretation of big earth observation data is a tedious process. Future prospects of this research include the implementation of the proposed methods in various remote sensing applications.Ο στόχος της παρούσας διατριβής ήταν η διερεύνηση και υλοποίηση καινοτόμων μεθόδων Όρασης Υπολογιστών και Υπολογιστικής Νοημοσύνης στα πλαίσια της μεθοδολογίας της Αντικειμενοστρεφούς Ανάλυσης Εικόνας (OBIA). Επίσης στόχος ήταν η ολοκλήρωση των μεθόδων αυτών με τεχνικές βασισμένες στη γνώση, δηλαδή με Έμπειρα Συστήματα. Η πρώτη συνεισφορά της διατριβής αφορούσε στην υλοποίηση ενός πολυκλιμακωτού αλγορίθμου κατάτμησης εικόνας, ο οποίος μπορεί να ενσωματωθεί σε μεθοδολογίες Αντικειμενοστρεφούς Ανάλυσης Εικόνας. Ο αλγόριθμος που αναπτύχθηκε, ονομάστηκε MSEG και είναι ένας αλγόριθμος της κατηγορίας ένωσης περιοχών (region merging). Η δεύτερη συνεισφορά της διατριβής ήταν η διερεύνηση και υλοποίηση ενός πολυκλιμακωτου αλγορίθμου κατάτμησης εικόνας, βασισμένου σε αύξηση περιοχών, με την ολοκλήρωση προηγμένων τεχνικών υφής. Η τρίτη συνεισφορά της διατριβής ήταν η ολοκλήρωση ενός αλγορίθμου υπολογιστικής νοημοσύνης, των Μηχανών Διανυσματικής Υποστήριξης (Support Vector Machines) στα πλαίσια της Αντικειμενοστρεφούς Ανάλυσης Εικόνας. Οι Μηχανές Διανυσματικής Υποστήριξης θεωρούνται μια άριστη μέθοδος υπολογιστικής μάθησης με εξαιρετικά αποτελέσματα στην Αναγνώριση Προτύπων. Ειδικά σε προβλήματα επιβλεπόμενης ταξινόμησης σε μεγάλους χώρους προτύπων, έχει αποδειχθεί ότι είναι μια από τις αποτελεσματικότερες μεθόδους με πολύ καλά αποτελέσματα. Σε αυτή τη διατριβή για πρώτη φορά υλοποιήθηκε Αντικειμενοστρεφής Ταξινόμηση Εικόνας μέσω της τεχνολογίας των Μηχανών Διανυσματικής Υποστήριξης με σκοπό την διερεύνηση της αποτελεσματικότητάς τους και της προοπτικής τους σαν μια μεθοδολογία αιχμής. Η τέταρτη συνεισφορά της διατριβής ήταν μια μεθοδολογία Αντικειμενοστρεφούς Ανάλυσης Εικόνας με ενσωμάτωση προηγμένων τεχνικών μη ισοτροπικής διάχυσης και φιλτραρισμάτων χώρου-κλίμακας. Τα φιλτραρίσματα χώρου-κλίμακας ενσωματώθηκαν στη διαδικασία κατάτμησης εικόνας βελτιώνοντας τα αποτελέσματα τόσο της κατάτμησης όσο και της μετέπειτα ταξινόμησης. Η πέμπτη συνεισφορά της διατριβής αφορούσε στην ολοκλήρωση προηγμένων τεχνικών ανίχνευσης ακμών στην διαδικασία κατάτμησης εικόνας για την υλοποίηση μεθοδολογίας Αντικειμενοστρεφούς Ανάλυσης Εικόνας. Ο συνδιασμός των μορφολογικών επιπεδοσυνόλων, της πολυκλιμακωτής κατάτμησης και της πληροφορίας ακμών της εικόνας έδωσε μια νέα υβριδική προσέγγιση στην κατάτμηση εικόνας. Η έκτη συνεισφορά της διατριβής αφορούσε την υλοποίηση Αντικειμενοστρεφούς Ταξινόμησης με μεθόδους που βρίσκονται στην αιχμή της επιστήμης της Υπολογιστικής Νοημοσύνης. Για πρώτη φορά προτάθηκε στην διατριβή αυτή η υλοποίηση αντικειμενοστρεφούς μεθόδου ταξινόμησης με βάση τις Μηχανές Διανυσμάτων Συνάφειας (Relevance Vector Machines). Τέλος η έβδομη συνεισφορά της διατριβής αφορούσε στην ολοκλήρωση της Αντικειμενοστρεφούς Ανάλυσης Εικόνας με συστήματα που βασίζονται στη γνώση. Στα πλαίσια αυτής της έρευνας, στόχος ήταν η διασύνδεση των προηγμένων μεθόδων Υπολογιστικής Νοημοσύνης με έμπειρα συστήματα που βασίζονται στη γνώση ώστε να διερευνηθεί και να αξιολογηθεί η χρησιμότητά τους σε εφαρμογές Τηλεπισκόπησης. Με βάση τις προηγούμενες μεθοδολογίες της διατριβής, χρησιμοποιήθηκε ο αλγόριθμος κατάτμησης εικόνας με βάση τις ακμές για την εξαγωγή πρωτογεννών αντικειμένων, ενώ μετά τον ορισμό του χώρου προτύπων από τον υπολογισμό χαρακτηριστικών των αντικειμένων, χρησιμοποιήθηκαν οι Μηχανές Διανυσμάτων Συνάφειας για την αρχική ταξινόμηση των αντικειμένων με βάση δείγματα εκπαίδευσης. Στην συνέχεια τα αποτελέσματα της κατάτμησης και της ταξινόμησης των αντικειμένων ενσωματώθηκαν σε μια χωρική βάση δεδομένων με τον μετασχηματισμό τους από εικονιστικά σε διανυσματικά δεδομένα και αποθηκεύοντας τα χαρακτηριστικά τους με τη μορφή πινάκων που ενσωματώνουν πληροφορία γεωμετρίας. Σε γενικές γραμμές η διατριβή είχε συνεισφόρα σε όλες τις διαδικασίες της Αντικειμενοστρεφούς Ανάλυσης Εικόνας και η ενσωμάτωση μεθόδων όρασης υπολογιστών, κατάτμησης εικόνας, υπολογιστικής νοημοσύνης και εμπείρων συστημάτων ολοκληρώνει τις δυνατότητες ενός σύγχρονου τηλεπισκοπικού συστήματος, προσδίδοντάς του πολλές δυνατότητες αυτοματοποίησης επίπονων διαδικασιών για τον φωτοερμηνευτή. Μελλοντικά, ανοίγονται οι ορίζοντες για χρήση της προτεινόμενης μεθοδολογίας σε πιο συγκεκριμένες εφαρμογές Τηλεπισκόπησης