Synthetic aperture radar analysis of floating ice at Terra Nova Bay-an application to ice eddy parameter extraction

In the framework of a study of ice formation in Antarctica, synthetic aperture radar (SAR) image acquisitions were planned over Terra Nova Bay (TNB). Thanks to the European Space Agency (ESA) Third Party Mission program, Cosmo-SkyMed and Radarsat-2 images over TNB were obtained for the period of February 20 to March 20, 2015; in addition, available Sentinel-1 images for the same period were retrieved from the ESA scientific data hub. The first inspection of the images revealed the presence of a prominent eddy, i.e., an ice vortex presumably caused by the wind blowing from the continent. The important parameters of an eddy are its area and lifetime. While the eddy lifetime was easily obtained from the image sequence, the area was measured using a specific processing scheme that consists of nonlinear filtering and Markov random field segmentation. The main goal of our study was to develop a segmentation scheme to detect and measure "objects" in SAR images. In addition, the connection between eddy area and wind field was investigated using parametric and nonparametric correlation functions; statistically significant correlation values were obtained in the analyzed period. After March 15, a powerful katabatic wind completely disrupted the surface eddy

Maximum Likelihood Estimation of Exponentials in Unknown Colored Noise for Target Identification in Synthetic Aperture Radar Images

This dissertation develops techniques for estimating exponential signals in unknown colored noise. The Maximum Likelihood (ML) estimators of the exponential parameters are developed. Techniques are developed for one and two dimensional exponentials, for both the deterministic and stochastic ML model. The techniques are applied to Synthetic Aperture Radar (SAR) data whose point scatterers are modeled as damped exponentials. These estimated scatterer locations (exponentials frequencies) are potential features for model-based target recognition. The estimators developed in this dissertation may be applied with any parametrically modeled noise having a zero mean and a consistent estimator of the noise covariance matrix. ML techniques are developed for a single instance of data in colored noise which is modeled in one dimension as (1) stationary noise, (2) autoregressive (AR) noise and (3) autoregressive moving-average (ARMA) noise and in two dimensions as (1) stationary noise, and (2) white noise driving an exponential filter. The classical ML approach is used to solve for parameters which can be decoupled from the estimation problem. The remaining nonlinear optimization to find the exponential frequencies is then solved by extending white noise ML techniques to colored noise. In the case of deterministic ML, the computationally efficient, one and two-dimensional Iterative Quadratic Maximum Likelihood (IQML) methods are extended to colored noise. In the case of stochastic ML, the one and two-dimensional Method of Direction Estimation (MODE) techniques are extended to colored noise. Simulations show that the techniques perform close to the Cramer-Rao bound when the model matches the observed noise

Maximum Likelihood Estimation of Exponentials in Unknown Colored Noise for Target in Identification Synthetic Aperture Radar Images

This dissertation develops techniques for estimating exponential signals in unknown colored noise. The Maximum Likelihood ML estimators of the exponential parameters are developed. Techniques are developed for one and two dimensional exponentials, for both the deterministic and stochastic ML model. The techniques are applied to Synthetic Aperture Radar SAR data whose point scatterers are modeled as damped exponentials. These estimated scatterer locations exponentials frequencies are potential features for model-based target recognition. The estimators developed in this dissertation may be applied with any parametrically modeled noise having a zero mean and a consistent estimator of the noise covariance matrix. ML techniques are developed for a single instance of data in colored noise which is modeled in one dimension as 1 stationary noise, 2 autoregressive AR noise and 3 autoregressive moving-average ARMA noise and in two dimensions as 1 stationary noise, and 2 white noise driving an exponential filter. The classical ML approach is used to solve for parameters which can be decoupled from the estimation problem. The remaining nonlinear optimization to find the exponential frequencies is then solved by extending white noise ML techniques to colored noise. In the case of deterministic ML, the computationally efficient, one and two-dimensional Iterative Quadratic Maximum Likelihood IQML methods are extended to colored noise. In the case of stochastic ML, the one and two-dimensional Method of Direction Estimation MODE techniques are extended to colored noise. Simulations show that the techniques perform close to the Cramer-Rao bound when the model matches the observed noise

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

APPLYING DEEP LEARNING METHODS TO IDENTIFY TARGETS IN SYNTHETIC APERTURE RADAR IMAGES

Synthetic aperture radar (SAR) provides high-resolution imagery and can operate in the day and at night and in every weather condition. SAR has been used for military reconnaissance and surveillance. Examining SAR images manually, however, is challenging even for a specialist, since it is difficult to find high-value targets in a wide area of SAR images. This is especially true when time is critical for operations. Thus, an efficient, reliable method to analyze SAR images automatically is needed. To solve this problem, deep learning (DL) methods are developed for automatic target recognition (ATR). A convolutional neural network (CNN) is a deep-learning algorithm made up of several processing layers for target recognition and classification. One of the challenges in developing and testing a CNN algorithm is to find relevant datasets. The dataset used in this thesis comes from the Moving and Stationary Target Acquisition and Recognition program (MSTAR). In this research, the SAR ATR concept and performance are analyzed using several CNN DL architectures. Specifically, this investigation examines the effects of a few variable parameters within CNN DL architectures to gain insight into optimal strategies for using these architectures. Using CNN structures with different numbers of layers, it was possible to classify SAR targets successfully and automatically with state-of-the-art accuracy. This method proved useful for classification and recognition of military targets.Captain, Turkish Air ForceApproved for public release. distribution is unlimite

Deep learning-based vessel detection from very high and medium resolution optical satellite images as component of maritime surveillance systems

This thesis presents an end-to-end multiclass vessel detection method from optical satellite images. The proposed workflow covers the complete processing chain and involves rapid image enhancement techniques, the fusion with automatic identification system (AIS) data, and the detection algorithm based on convolutional neural networks (CNN). The algorithms presented are implemented in the form of independent software processors and integrated in an automated processing chain as part of the Earth Observation Maritime Surveillance System (EO-MARISS).In der vorliegenden Arbeit wird eine Methode zur Detektion von Schiffen unterschiedlicher Klassen in optischen Satellitenbildern vorgestellt. Diese gliedert sich in drei aufeinanderfolgende Funktionen: i) die Bildbearbeitung zur Verbesserung der Bildeigenschaften, ii) die Datenfusion mit den Daten des Automatischen Identifikation Systems (AIS) und iii) dem auf „Convolutional Neural Network“ (CNN) basierenden Detektionsalgorithmus. Die vorgestellten Algorithmen wurden in Form eigenständiger Softwareprozessoren implementiert und als Teil des maritimen Erdbeobachtungssystems integriert