Soil moisture estimation from Sentinel-1 interferometric observations over arid regions

We present a methodology based on interferometric synthetic aperture radar (InSAR) time series analysis that can provide surface (top 5 cm) soil moisture (SSM) estimations. The InSAR time series analysis consists of five processing steps. A co-registered Single Look Complex (SLC) SAR stack as well as meteorological information are required as input of the proposed workflow. In the first step, ice/snow-free and zero-precipitation SAR images are identified using meteorological data. In the second step, construction and phase extraction of distributed scatterers (DSs) (over bare land) is performed. In the third step, for each DS the ordering of surface soil moisture (SSM) levels of SAR acquisitions based on interferometric coherence is calculated. In the fourth step, for each DS the coherence due to SSM variations is calculated. In the fifth step, SSM is estimated by a constrained inversion of an analytical interferometric model using coherence and phase closure information. The implementation of the proposed approach is provided as an open-source software toolbox (INSAR4SM) available at www.github.com/kleok/INSAR4SM. A case study over an arid region in California/Arizona is presented. The proposed workflow was applied in Sentinel- 1 (C-band) VV-polarized InSAR observations. The estimated SSM results were assessed with independent SSM observations from a station of the International Soil Moisture Network (ISMN) (RMSE: 0.027 $m^3/m^3$ R: 0.88) and ERA5-Land reanalysis model data (RMSE: 0.035 $m^3/m^3$ R: 0.71). The proposed methodology was able to provide accurate SSM estimations at high spatial resolution (~250 m). A discussion of the benefits and the limitations of the proposed methodology highlighted the potential of interferometric observables for SSM estimation over arid regions

Estimation of the Number of Endmembers Using Robust Outlier Detection Method

This paper introduces a novel approach for estimating the numbers of endmembers in hyperspectral imagery. It exploits the geometrical properties of the noise hypersphere and considers the signal as outlier of the noise hypersphere. The proposed method, called outlier detection method (ODM), is automatic and non-parametric. In a principal component space, noise is spherically symmetric in all directions and lies on the surface of a hypersphere with a constant radius. Reversely, signal radiuses are much larger that noise radius and vary in all directions, thus signal lies in a hyperellipsoid. The proposed method involves three steps: 1) noise estimation; 2) minimum noise fraction transformation; and 3) outlier detection using inter quartile range. Estimation of the number of endmembers is accomplished by the estimation of the number of noise hypersphere outliers using a robust outlier detection method. The ODM was evaluated using simulated and real hyperspectral data, and it was also compared with well-known methods for estimating the number of endmembers. Evaluation of the method showed that the method produces robust and satisfactory results, and outperforms in relation to its competitors

CONTRIBUTIONS TOWARDS THE IMPROVEMENT OF REMOTE SENSING ANALYSIS METHODS AND MAPPING OF LAND USE IN AGRICULTURAL REGIONS

IN THIS DISSERTATION AN ATTEMPT IS MADE TO INVESTIGATE THE POTENTIAL OF IMPROVING THE DIGITAL IMAGE PROCESSING METHODS AND TECHNIQUES, BY THE INCLUSION OF ANALOGUE IMAGES PHOTOINTERPRETATION, WITH THE AIM TO HELP INTERPRETATION AND MAPPING OF LAND USE IN THE AGRARIAN REGIONS OF GREECE WHICH ARE CHARACTERISED BY A LARGE NUMBER OF SMALL LAND PARCELS AND ARBITARILY S OWNERSHIP RIGHTS. THUS, A NEW DEVELOPED CLASSIFICATION METHOD WAS DEVELOPED WHICH IS BASED ON THE DETERMINISTIC APPROACH, APPLIES NEURAL NETWORK TECHNIQUES AND USES IMAGES WHICH RESULT FROM INDEXES APPLICATION. SO AN INFERENCE ENGINE WAS DETERMINED WHICH CONSTITUTES THE EMPIRICAL PART OF THE METHOD. THE KNOWLEDGE BASE OF THE METHOD, WHICH FORMS THE NUMERICAL PART OF THE METHOD, WAS CONSTRUCTED BY USING TECHNIQUES OF NEURAL NETWORK. SO, THE "PERCEPTRON" LEARNING ALGORITHM WAS USED. THE NEW CLASSIFICATION METHOD IMPROVED THE CLASSIFICATION ACCURACY RESULTING IN AN INCREASE OF10%, WHEN COMPARED WITH MAXIMUM LIVELIHOOD CLASSIFICATION ACCURACIES APPLIED IN THE SAME REGION.Η ΔΙΔΑΚΤΟΡΙΚΗ ΑΥΤΗ ΔΙΑΤΡΙΒΗ, ΔΙΕΡΕΥΝΑ ΤΟ ΒΑΘΜΟ ΚΑΙ ΤΑ ΟΡΙΑ ΑΚΡΙΒΕΙΑΣ ΤΩΝ ΦΩΤΟΕΡΜΗΝΕΥΤΙΚΩΝ ΚΑΙ ΤΗΛΕΠΙΣΚΟΠΙΚΩΝ ΜΕΘΟΔΩΝ ΚΑΙ ΤΕΧΝΙΚΩΝ ΓΙΑ ΤΗΝ ΑΠΟΓΡΑΦΗ ΤΩΝ ΧΡΗΣΕΩΝΓΗΣ ΜΙΑΣ ΑΓΡΟΤΙΚΗΣ ΠΕΡΙΟΧΗΣ ΤΗΣ ΕΛΛΑΔΑΣ Η ΟΠΟΙΑ ΧΑΡΑΚΤΗΡΙΖΕΤΑΙ, ΟΠΩΣ ΚΑΙ ΣΤΟ ΣΥΝΟΛΟ ΤΗΣ ΑΛΛΩΣΤΕ Η ΧΩΡΑ ΜΑΣ, ΑΠΟ ΕΝΑΛΛΑΓΗ ΤΟΥ ΑΝΑΓΛΥΦΟΥ, ΚΑΙ ΜΙΚΡΕΣ ΔΙΕΣΠΑΡΜΕΝΕΣ ΙΔΙΟΚΤΗΣΙΕΣ ΜΕ ΠΟΙΚΙΛΙΑ ΚΑΛΛΙΕΡΓΕΙΩΝ. ΕΤΣ ΜΙΑ ΒΕΛΤΙΩΜΕΝΗ ΜΕΘΟΔΟΣ ΤΑΞΙΝΟΜΙΣΗΣ, Η ΟΠΟΙΑ ΑΞΙΟΠΟΙΕΙ ΤΗ ΣΥΝΑΡΘΡΩΣΗ ΤΩΝ ΔΥΝΑΤΟΤΗΤΩΝ ΤΗΣ ΦΩΤΟΕΡΜΗΝΕΥΤΙΚΗΣ ΜΕΘΟΔΟΛΟΓΙΑΣ ΚΑΙ ΤΗΣ ΚΥΒΕΡΝΗΤΙΚΗΣ ΟΠΩΣ ΑΥΤΕΣ ΔΙΑΠΕΡΝΟΥΝ ΑΠΟ ΤΑ ΜΟΝΤΕΡΝΑ ΠΕΔΙΑ ΤΗΣ ΠΛΗΡΟΦΟΡΙΚΗΣ, ΑΦΟΡΑ ΣΤΗΝ ΤΑΞΙΝΟΜΗΣΗ Τ ΒΑΣΙΖΕΤΑΙ ΣΤΗΝ ΑΞΙΟΠΟΙΗΣΗ ΤΩΝ ΑΠΕΙΚΟΝΙΣΕΩΝ ΠΟΥΠΡΟΚΥΠΤΟΥΝ ΑΠΟ ΤΗΝ ΕΦΑΡΜΟΓΗ ΤΩΝ ΠΙΟ ΕΝΔΕΙΚΝΥΟΜΕΝΩΝ ΔΕΙΚΤΩΝ ΚΑΙ ΥΙΟΘΕΤΕΙ ΤΗΝ ΑΙΤΙΟΚΡΑΤΙΚΗ ΠΡΟΣΕΓΓΙΣΗ. Η ΥΛΟΠΟΙΗΣΗ ΤΗΣ ΑΙΤΙΟΚΡΑΤΙΚΗΣ ΠΡΟΣΕΓΓΙΣΗΣ ΕΠΙΤΥΓΧΑΝΕΤΑΙ ΒΑΣΕΙ ΕΝΟΣ "ΔΙΚΤΥΟΥ ΔΡΑΣΤΗΡΙΟΤΗΤΩΝ", ΤΟΥ ΟΠΟΙΟΥ ΟΙ ΔΙΑΔΡΟΜΕΣ ΑΠΟΤΕΛΟΥΝ ΤΟ ΜΗΧΑΝΙΣΜΟ ΕΠΑΓΩΓΗΣ ΤΗΣ ΜΕΘΟΔΟΥ. Η ΒΑΣΗ ΓΝΩΣΗΣ ΤΗΣ ΜΕΘΟΔΟΥ ΚΑΤΑΡΤΙΖΕΤΑΙ ΜΕΣΩ ΤΩΝ ΠΡΟΣΟΜΟΙΩΣΕΩΝ ΔΥΟ ΤΟΠΙΚΩΝ ΤΕΧΝΗΤΩΝ ΝΕΥΡΩΝΙΚΩΝ ΔΙΚΤΥΩΝ ΤΑ ΟΠΟΙΑ ΧΡΗΙΜΟΠΟΙΟΥΝ ΩΣ ΑΛΓΟΡΙΘΜΟ ΜΑΘΗΣΗΣ ΤΟΝ ΑΛΓΟΡΙΘΜΟ "PERCEPTRON". Η ΜΕΘΟΔΟΣ ΑΥΤΗ ΣΥΓΚΡΙΘΗΚΕ ΚΙ ΕΛΕΓΧΘΗΚΕ ΑΝΑΛΥΤΙΚΑ ΜΕ ΤΙΣ ΚΛΑΣΣΙΚΕΣ ΜΕΘΟΔΟΥΣ ΨΗΦΙΑΚΗΣ ΤΑΞΙΝΟΜΗΣΗΣ ΤΗΛΕΠΙΣΚΟΠΙΚΩΝ ΑΠΕΙΚΟΝΙΣΕΩΝ ΚΑΙ ΕΔΩΣΕ ΚΑΤΑ 10% ΠΕΡΙΠΟΥ ΚΑΛΥΤΕΡΑ ΑΠΟΤΕΛΕΣΜΑΤΑ

Oil Spill Detection and Mapping Using Sentinel 2 Imagery

Two object-based image analysis methods are developed for detecting oil spills from known natural outflows as well as light oil spill events using Sentinel 2 imagery. The methods are applied to Sentinel 2 images of a known area of natural oil outflow as well as on a Sentinel 2 image of a recent oil spill event along the south coast of Athens, Greece. The preliminary results are considered very successful and consistent, with a high degree of applicability to other Sentinel 2 satellite images. Further testing and fine tuning of the proposed object-based methodology should be carried out using atmospheric correction and ground truth

Fine-Tuning Self-Organizing Maps for Sentinel-2 Imagery: Separating Clouds from Bright Surfaces

Removal of cloud interference is a crucial step for the exploitation of the spectral information stored in optical satellite images. Several cloud masking approaches have been developed through time, based on direct interpretation of the spectral and temporal properties of clouds through thresholds. The problem has also been tackled by machine learning methods with artificial neural networks being among the most recent ones. Detection of bright non-cloud objects is one of the most difficult tasks in cloud masking applications since spectral information alone often proves inadequate for their separation from clouds. Scientific attention has recently been redrawn on self-organizing maps (SOMs) because of their unique ability to preserve topologic relations, added to the advantage of faster training time and more interpretative behavior compared to other types of artificial neural networks. This study evaluated a SOM for cloud masking Sentinel-2 images and proposed a fine-tuning methodology to separate clouds from bright land areas. The fine-tuning process which is based on the output of the non-fine-tuned network, at first directly locates the neurons that correspond to the misclassified pixels. Then, the incorrect labels of the neurons are altered without applying further training. The fine-tuning method follows a general procedure, thus its applicability is broad and not confined only in the field of cloud-masking. The network was trained on the largest publicly available spectral database for Sentinel-2 cloud masking applications and was tested on a truly independent database of Sentinel-2 cloud masks. It was evaluated both qualitatively and quantitatively with the interpretation of its behavior through multiple visualization techniques being a main part of the evaluation. It was shown that the fine-tuned SOM successfully recognized the bright non-cloud areas and outperformed the state-of-the-art algorithms: Sen2Cor and Fmask, as well as the version that was not fine-tuned

Hyperspectral data and methods for coastal water mapping

Motivated by the increasing importance of hyperspectral remote sensing, this study investigates the potential of the current-generation satellite hyperspectral data for coastal water mapping. Two narrow-band Hyperion images, acquired in summer 2004 within a nine day period, were used. The study area is situated at the northern sector of south Evvoikos Gulf, in Central Greece. Underwater springs, inwater streams, urban waste and industrial waste are present in the gulf. Thus, further research regarding the most appropriate methods for coastal water mapping is advisable. In situ measurements with a GPS have located the positions of all sources of water and waste. At these positions groundspectro-radiometer measurements were also implemented. Two different approaches were used for the reduction of the Hyperion bands. First, on the basis of histogram statistics the uncalibrated bands were selected and removed. Then the Minimum Noise Fraction was used to classify the bands according to their signal to noise ratio. The noisiest bands were removed and thirty-eight bands were selected for further processing. Second, mathematical and statistical criteria were applied to the in situ radiometer measurements of reflectance and radiance in order to identify the most appropriate parts of the spectrum for the detection of underwater springs and urban waste. This approach has determined nine hyperspectral bands. The Pixel Purity Index and the n-D Visualiser methods were used for the identification of the spectra endmembers. Both whole (Spectral Angle Mapper or Spectral Feature Fitting) and sub pixel methods (Linear Unmixing or Mixture-Tuned Matched Filtering) were used for further analysis and classification of the data. Bands resulting from processing the groundspectro-radiometer measurements produced the highest classification results. The spatial resolution of the Hyperion hyperspectral data hardly allows the detection and classification of underwater springs. Contrary, inwater streams and chlorophyll are satisfactorily classified. The SAM classification method seems to work better as the number of endmembers increases. The Linear Unmixing classification method gives better results as the number of endmembers decreases

Large-Scale Feature Selection Using Evolved Neural Networks

In this paper computational intelligence, referring here to the synergy of neural networks and genetic algorithms, is deployed in order to determine a near-optimal neural network for the classification of dark formations in oil spills and look-alikes. Optimality is sought in the framework of a multi-objective problem, i.e. the minimization of input features used and, at the same time, the maximization of overall testing classification accuracy. The proposed method consists of two concurrent actions. The first is the identification of the subset of features that results in the highest classification accuracy on the testing data set i.e. feature selection. The second parallel process is the search for the neural network topology, in terms of number of nodes in the hidden layer, which is able to yield optimal results with respect to the selected subset of features. The results show that the proposed method, i.e. concurrently evolving features and neural network topology, yields superior classification accuracy compared to sequential floating forward selection as well as to using all features together. The accuracy matrix is deployed to show the generalization capacity of the discovered neural network topology on the evolved sub-set of features.JRC.G.6-Sensors, radar technologies and cybersecurit

Dark Formation Detection Using Neural Networks

Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in marine environment, as they are not affected by local weather conditions and cloudiness. Dark formations can be caused by man-made actions (e.g. oil spills) or natural ocean phenomena (e.g. natural slicks and wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they dampen the capillary and short gravity sea waves. Thus, traditionally, dark formation detection is the first stage of the oil-spill detection procedure and in most studies is performed manually or using a fixed size window in which a threshold value is adopted. In high-resolution imagery, dark formation detection may fail due to the nonlinear behaviour of the pixel values contained in the dark formation and in the area around it. In this paper, we examine the ability of two feed-forward neural network families, i.e. Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) networks, to detect dark formations in high-resolution SAR images. The general objective of this paper is to test the potential of artificial neural networks for dark formation detection using SAR high-resolution satellite images. Both the type and the architecture of the network are subjects of research. The inputs into the networks are the original SAR images. Each network is called to classify an area of the image as dark area or sea. The group of MLP networks can be recognized as the most suitable group for dark formation detection, as it presents reliable stable results for all the examined accuracies. Nevertheless, in terms of single topology, there is no an MLP topology that performs significantly better than the others.JRC.G.4-Maritime affair

Detection and Discrimination between Oil Spills and Look-Alike Phenomena through Neural Networks

Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in the marine environment, as their recording is independent of clouds and weather. Dark formations can be caused by man made actions (e.g. oil spill discharging) or natural ocean phenomena (e.g. natural slicks, wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they damp the capillary and short gravity sea waves. The ability of neural networks to detect dark formations in high resolution SAR images and to discriminate oil spills from lookalike phenomena simultaneously was examined. Two different neural networks are used; one to detect dark formations and the second one to perform a classification to oil spills or look-alikes. The proposed method is very promising in detecting dark formations and discriminating oil spills from look-alikes as it detects with an overall accuracy of 94% the dark formations and discriminate correctly 89% of examined cases.JRC.G.4-Maritime affair

A New Object-oriented Methodology to Detect Oil Spills using ENVISAT Images

Several ASAR images from ENVISAT were tested for oil spill detection using a new object oriented approach. A new automated methodology for oil spill detection were previously introduced, by which full SAR high resolution image scenes can be processed. In the present paper the method is tested using full high resolution ENVISAT data. The methodology relies on the object oriented approach and profits of image segmentation techniques in order for dark formations to be detected. The detection of dark formations is based on a threshold definition which is fully adaptive to local contrast and brightness of large image segments. For the detection process, two empirical formulas were developed, which also permit the classification of oil spills according to their brightness. A fuzzy classification method is used to classify dark formations to oils spill or look-alikes. Dark formations are not isolated and features of both dark areas and sea environment are considered. Various sea environments which affect oil spill shape and boundaries are grouped in two knowledge bases, used for the classification of dark formations. The method’s accuracy was tested for ENVISAT images. Previously test for 12 ERS images saw more than 99% for oil spill accuracy, and close to 99% for look-alike accuracy. Fresh oil spills, fresh spills affected by natural phenomena, oil spills without clear stripping, small linear oil spills, oil spills with broken parts and amorphous oil spills can be successfully detected.JRC.G.4-Maritime affair