Semantic segmentation using deep neural networks for SAR and optical image pairs

Semantic segmentation for synthetic aperture radar (SAR) imagery is a rarely touched area, due to the specific image characteristics of SAR images. In this research, we propose a dataset which consists of three data sources: TerraSAR-X images, Google Earth images and OpenStreetMap data, with the purpose of performing SAR and optical image semantic segmentation. By using fully convolutional networks and deep residual networks with pre-trained weights, we investigate the accuracy and mean IOU values of semantic segmentation for both SAR and optical image patches. The best Segmentation accuracy results for SAR and optical data are around 74% and 82%. Moreover, we study SAR models by combining multiple data sources: Google Earth images and OpenStreetMap data

Ηλεκτροχημική επεξεργασία βιομηχανικών αποβλήτων με χωρητικό απιονισμό, ηλεκτροκροκίδωση και ηλεκτροοξείδωση

The present thesis covers a significant part of the research field of processing industrial wastewater by means of electrochemical processes.The under treatment sample of liquid waste is placed in an electrochemical reactor, where several electrochemical processes occur due to transit of electricity and chemical transformations, caused either directly at the interface solution / electrode or indirectly by subsequent physicochemical actions in the main solution.The main purpose of this study was to explore the use of electrochemical processes as basic industrial wastewater treatment methods. Several electrochemical methods were studied for their efficacy, such as capacitive deionization with nano-porous carbon electrodes, electrocoagulation with aluminum and iron electrodes, electrooxidation with different types of electrodes, and the specific electrooxidation electro-Fenton.As target-pollutants were a) heavy metals in plating waste outputs b) dyes in wastewater from paintshops c) hydrogen sulfide and hydrocarbon residues in oily waste derived from petroleum treatment. Additionally, the removal of NaCl in aqueous solutions was studied.Based on the experimental results, was deduced that the employed electrochemical methods constitute a safe and economical way of removing pollutants from both simulated synthetic aqueous solutions as well as from real liquid industrial waste.The waste processing by means of Electrocoagulation achieved rapid and effective reduction of heavy metals and hydrogen sulfide by 99.9%. By using the same method in aqueous solutions containing dyes, the concentration of pigments decreased below the detection limits while in tanker truck wastes the use of surfactants along with electrooxidation led to a 90% abatement of the hydrocarbon concentration.Moreover, the capacitive deionization demonstrated an economical and promising method for the desalination and deionization of water.In addition, for the production of the required electricity in some Electrocoagulation experiments photovoltaic panels were employed. In this way the electrochemical waste purification was achieved via using renewable energy, so as it to become environmentally friendly and autonomous (especially for remote areas and islands).Η παρούσα διδακτορική διατριβή καλύπτει σημαντικό μέρος του ερευνητικού πεδίου της επεξεργασίας υγρών βιομηχανικών αποβλήτων με χρήση ηλεκτροχημικών διεργασιών. Το προς επεξεργασία δείγμα υγρού αποβλήτου τοποθετείται στον ηλεκτροχημικό αντιδραστήρα, όπου λαμβάνουν χώρα οι διάφορες ηλεκτροχημικές διεργασίες λόγω της διέλευσης του ηλεκτρικού ρεύματος και των χημικών μετατροπών που προκαλούνται είτε άμεσα στην διεπιφάνεια διαλύματος/ηλεκτροδίου είτε έμμεσα με επακόλουθες φυσικοχημικές δράσεις στο κυρίως διάλυμα. Ο βασικός σκοπός της εργασίας είναι η διερεύνηση της χρήσης ηλεκτροχημικών διεργασιών ως βασικών μεθόδων στην επεξεργασία υγρών βιομηχανικών αποβλήτων. Γι’ αυτό το σκοπό μελετήθηκε η αποτελεσματικότητα των ηλεκτροχημικών μεθόδων , όπως ο χωρητικός απιονισμός (capacitive deionization) με νανο-πορώδη ηλεκτρόδια άνθρακα, η ηλεκτροκροκίδωση (electrocoagulation) με ηλεκτρόδια αλουμινίου και σιδήρου, η ηλεκτροοξείδωση (electrooxidation) με διάφορα είδη ηλεκτροδίων και η ειδική ηλεκτροοξείδωση ηλεκτρο-Φεντον (electro-Fenton). Ως ρύποι-στόχοι αποτέλεσαν α) τα βαρέα μέταλλα στις εκροές αποβλήτων επιμεταλλώσεων, β) οι χρωστικές στα υγρά απόβλητα βαφείων, γ) το υδρόθειο και τα υπολείμματα υδρογονανθράκων στα ελαιώδη απόβλητα επεξεργασίας πετρελαίου. Επιπρόσθετα μελετήθηκε η απομάκρυνση του NαCl από υδατικά διαλύματα. Από τα αποτελέσματα των πειραμάτων διαπιστώθηκε ότι οι ηλεκτροχημικές μέθοδοι που χρησιμοποιήθηκαν αποτελούν ασφαλή και οικονομικό τρόπο απομάκρυνσης των ρυπογόνων ουσιών τόσο από προσομοιωμένα συνθετικά υδατικά διαλύματα όσο και από πραγματικά υγρά βιομηχανικά απόβλητα. Η επεξεργασία αποβλήτων με ηλεκτροκροκίδωση πέτυχε γρήγορη και αποτελεσματική μείωση των βαρέων μετάλλων και του υδρόθειου κατά 99,9%. Με την ίδια μέθοδο σε υδατικά διαλύματα που περιείχαν χρωστικές ουσίες η συγκέντρωση των χρωστικών μειώθηκε κάτω από τα όρια ανίχνευσης ενώ σε απόβλητα πετρελαιοφόρων φορτηγών με την χρήση επιφανειοδραστικών ουσιών και ηλεκτροοξείδωση η συγκέντρωση των υδρογονανθράκων μειώθηκε κατά 90%. Επίσης ο χωρητικός απιονισμός αποδείχθηκε μία οικονομική και πολλά υποσχόμενη μέθοδος για την αφαλάτωση και τον απιονισμό του νερού. Επιπλέον, για την παραγωγή της απαιτούμενης ηλεκτρικής ενέργειας σε ορισμένα πειράματα ηλεκτροκροκίδωσης χρησιμοποιήθηκαν φωτοβολταικά πάνελς. Με αυτό τον τρόπο επιτυγχάνεται ο ηλεκτροχημικός καθαρισμός αποβλήτων με την χρήση ανανεώσιμων μορφών ενέργειας. Έτσι η διαδικασία καθαρισμού γίνεται φιλική προς το περιβάλλον και αυτόνομη (ιδιαίτερα για απομακρυσμένες περιοχές και νησιά)

Deep Learning Earth Observation Classification Using ImageNet Pre-trained Networks

Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate classification results when provided with large enough data sets and respective labels. However, using CNNs along with limited labeled data can be problematic, as this leads to extensive overfitting. In this letter, we propose a novel method by considering a pretrained CNN designed for tackling an entirely different classification problem, namely, the ImageNet challenge, and exploit it to extract an initial set of representations. The derived representations are then transferred into a supervised CNN classifier, along with their class labels, effectively training the system. Through this two-stage framework, we successfully deal with the limited-data problem in an end-to-end processing scheme. Comparative results over the UC Merced Land Use benchmark prove that our method significantly outperforms the previously best stated results, improving the overall accuracy from 83.1% up to 92.4%. Apart from statistical improvements, our method introduces a novel feature fusion algorithm that effectively tackles the large data dimensionality by using a simple and computationally efficient approach

A Novel Method For Building Height Estimation Using TanDEM-X Data

The main goal of the German TanDEM-X mission (TDM) is the provision of a global digital elevation model (DEM) at the unique spatial resolution of ~12m, which, compared to other freely available DEMs [e.g., STRM-DEM (90m), ASTER-GDEM (30m)], holds the potential to discriminate objects above the ground as, e.g., buildings. Accordingly, in this paper we present a novel unsupervised method for automatically estimating local height variations in built-up areas by means of the TanDEM-X DEM. In the first step, we identify points lying on the ground surface by means of a dedicated algorithm. In particular, the basic idea is that pixels associated with potential infrastructural elements can be identified and then excluded by analyzing the relative change in elevation with respect to their neighbors (indeed they locally exhibit greater height values with respect to ground pixels). Next, the remaining samples are used to generate a digital terrain model (DTM) of the investigated region by employing the Natural Neighbors (NN) interpolation algorithm. The local building height is finally estimated by subtracting the DTM from the original DEM. Preliminary experimental results obtained for the area of Dongying (China) which includes the Yellow River Delta (YRD) assess the effectiveness of the proposed approach and its potential to provide a reliable indication of the overall distribution of building height at large scale

Deep Neural Networks For Above-ground Detection in Very High Spatial Resolution Digital Elevation Models

Deep Learning techniques have lately received increased attention for achieving state-of-the-art results in many classification problems, including various vision tasks. In this work, we implement a Deep Learning technique for classifying above-ground objects within urban environments by using a Multilayer Perceptron model and VHSR DEM data. In this context, we propose a novel method called M-ramp which significantly improves the classifier’s estimations by neglecting artefacts, minimizing convergence time and improving overall accuracy. We support the importance of using the M-ramp model in DEM classification by conducting a set of experiments with both quantitative and qualitative results. Precisely, we initially train our algorithm with random DEM tiles and their respective point-labels, considering less than 0.1% over the test area, depicting the city center of Munich (25 km²). Furthermore with no additional training, we classify two much larger unseen extents of the greater Munich area (424 km²) and Dongying city, China (257 km²) and evaluate their respective results for proving knowledge-transferability. Through the use of M-ramp, we were able to accelerate the convergence by a magnitude of 8 and achieve a decrease in above-ground relative error by 24.8% and 5.5% over the different datasets

Urban classification from optical satellite images: a comparison between conventional machine learning and deep learning approaches

Urban classification is a challenging problem for many reasons; the diverse types of urban areas with different appearance in remotely sensed image data (residential areas, industrial infrastructures, sport facilities, etc.), the different look of urban areas from one country to another, or even within the same country. There have been many attempts to develop a global classifier for urban areas producing urban maps with various accuracy and diverse resolutions. To answer the question of whether it is possible to develop a single high accuracy global classifier which can classify urban areas worldwide, the first step would be to extract a good set of global features, the second would be to design a good generic classifier. In this study, both aspects of the problem were investigated. To this end, we followed two alternative approaches. The first alternative consists of conventional feature extraction (e.g., Gabor filtering, NDVI, local variance computation, etc.) followed by classification (e.g., Support Vector Machine). The second alternative is an end-to-end Deep Learning solution with no feature engineering. In our study, we explored and compared the performance of different feature extractors and different classifiers. As a first step towards solving the problem in a global scale, we will demonstrate typical cases of urban classification using LANDSAT ETM+ data of a large area in the eastern part of the USA, covering few cities and their surroundings in four side-by-side LANDSAT scenes. For ground truth we used settlement maps from national agency, in addition to the freely available data from OpenStreetMap. Our initial results show that Support Vector Machines (SVM) provide good results, while the deep learning model using Convolutional Neural Networks (CNNs) performs similarly well in local areas and generalizes better

Artificial generation of big data for improving image classification: a generative adversarial network approach on SAR data

Very High Spatial Resolution (VHSR) large-scale SAR image databases are still an unresolved issue in the Remote Sensing field. In this work, we propose such a dataset and use it to explore patch-based classification in urban and periurban areas, considering 7 distinct semantic classes. In this context, we investigate the accuracy of large CNN classification models and pre-trained networks for SAR imaging systems. Furthermore, we propose a Generative Adversarial Network (GAN) for SAR image generation and test, whether the synthetic data can actually improve classification accuracy

Semantic Segmentation of Aerial Images with Explicit Class-Boundary Modeling

In this work we propose an end-to-end trainable supervised Deep Convolutional Neural Network (DCNN) targeting the task of semantic-segmentation with the addition of class-aware boundary detection. Through this explicit modeling of the class-boundaries, we enforce the network to extract coherent and complete objects, suppressing the uncertainty influencing these regions. Importantly, we show that class-boundary networks in conjunction with DCNN performs optimally, achieving over 90% overall accuracy (OA) on the challenging ISPRS Vaihingen Semantic Segmentation benchmark