A False-alarm-controllable Modified AdaBoost Wake Detection Method Using SAR Images

A false-alarm-controllable modified AdaBoost-based method is proposed for detecting ship wake from sea clutter in synthetic aperture radar (SAR) images. It reformulates the wake detection problem as a binary classification task in the multifeature space. The update strategy of the sample weights in the original AdaBoost is modified for wake detection. First, a detection result confidence factor is designed to deal with class imbalance between sea clutter and ship wake; then, the AdaBoost is further modified as a false alarm rate (FAR) controllable detector by introducing penalty parameters to adjust weights update strategies for the sea clutter. Meanwhile, the multifeature space is spanned by a novel frequency peak height ratio (FPHA) feature and four salient features. FPHA is proposed to enhance the separation between the wake and sea clutter, which is computed from the amplitude spectrum peak of the image after the Fourier transform. Experimental results show that the proposed detector can tackle the imbalanced data problem and flexibly control FAR by adjusting penalty parameters. Moreover, improved detection probability is also achieved compared with existing methods

Optimized techniques for real-time microwave and millimeter wave SAR imaging

Microwave and millimeter wave synthetic aperture radar (SAR)-based imaging techniques, used for nondestructive evaluation (NDE), have shown tremendous usefulness for the inspection of a wide variety of complex composite materials and structures. Studies were performed for the optimization of uniform and nonuniform sampling (i.e., measurement positions) since existing formulations of SAR resolution and sampling criteria do not account for all of the physical characteristics of a measurement (e.g., 2D limited-size aperture, electric field decreasing with distance from the measuring antenna, etc.) and nonuniform sampling criteria supports sampling below the Nyquist rate. The results of these studies demonstrate optimum sampling given design requirements that fully explain resolution dependence on sampling criteria. This work was then extended to manually-selected and nonuniformly distributed samples such that the intelligence of the user may be utilized by observing SAR images being updated in real-time. Furthermore, a novel reconstruction method was devised that uses components of the SAR algorithm to advantageously exploit the inherent spatial information contained in the data, resulting in a superior final SAR image. Furthermore, better SAR images can be obtained if multiple frequencies are utilized as compared to single frequency. To this end, the design of an existing microwave imaging array was modified to support multiple frequency measurement. Lastly, the data of interest in such an array may be corrupted by coupling among elements since they are closely spaced, resulting in images with an increased level of artifacts. A method for correcting or pre-processing the data by using an adaptation of correlation canceling technique is presented as well --Abstract, page iii

Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

Privacy Preserving Domain Adaptation for Semantic Segmentation of Medical Images

Convolutional neural networks (CNNs) have led to significant improvements in tasks involving semantic segmentation of images. CNNs are vulnerable in the area of biomedical image segmentation because of distributional gap between two source and target domains with different data modalities which leads to domain shift. Domain shift makes data annotations in new modalities necessary because models must be retrained from scratch. Unsupervised domain adaptation (UDA) is proposed to adapt a model to new modalities using solely unlabeled target domain data. Common UDA algorithms require access to data points in the source domain which may not be feasible in medical imaging due to privacy concerns. In this work, we develop an algorithm for UDA in a privacy-constrained setting, where the source domain data is inaccessible. Our idea is based on encoding the information from the source samples into a prototypical distribution that is used as an intermediate distribution for aligning the target domain distribution with the source domain distribution. We demonstrate the effectiveness of our algorithm by comparing it to state-of-the-art medical image semantic segmentation approaches on two medical image semantic segmentation datasets

Bayesian super-resolution with application to radar target recognition

This thesis is concerned with methods to facilitate automatic target recognition using images generated from a group of associated radar systems. Target recognition algorithms require access to a database of previously recorded or synthesized radar images for the targets of interest, or a database of features based on those images. However, the resolution of a new image acquired under non-ideal conditions may not be as good as that of the images used to generate the database. Therefore it is proposed to use super-resolution techniques to match the resolution of new images with the resolution of database images. A comprehensive review of the literature is given for super-resolution when used either on its own, or in conjunction with target recognition. A new superresolution algorithm is developed that is based on numerical Markov chain Monte Carlo Bayesian statistics. This algorithm allows uncertainty in the superresolved image to be taken into account in the target recognition process. It is shown that the Bayesian approach improves the probability of correct target classification over standard super-resolution techniques. The new super-resolution algorithm is demonstrated using a simple synthetically generated data set and is compared to other similar algorithms. A variety of effects that degrade super-resolution performance, such as defocus, are analyzed and techniques to compensate for these are presented. Performance of the super-resolution algorithm is then tested as part of a Bayesian target recognition framework using measured radar data

Applications of SAR Interferometry in Earth and Environmental Science Research

This paper provides a review of the progress in regard to the InSAR remote sensing technique and its applications in earth and environmental sciences, especially in the past decade. Basic principles, factors, limits, InSAR sensors, available software packages for the generation of InSAR interferograms were summarized to support future applications. Emphasis was placed on the applications of InSAR in seismology, volcanology, land subsidence/uplift, landslide, glaciology, hydrology, and forestry sciences. It ends with a discussion of future research directions

Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling

Satellite-based Synthetic Aperture Radar (SAR) has proved useful for obtaining information on flood extent, which, when intersected with a Digital Elevation Model (DEM) of the floodplain, provides water level observations that can be assimilated into a hydrodynamic model to decrease forecast uncertainty. With an increasing number of operational satellites with SAR capability, information on the relationship between satellite first visit and revisit times and forecast performance is required to optimise the operational scheduling of satellite imagery. By using an Ensemble Transform Kalman Filter (ETKF) and a synthetic analysis with the 2D hydrodynamic model LISFLOOD-FP based on a real flooding case affecting an urban area (summer 2007,Tewkesbury, Southwest UK), we evaluate the sensitivity of the forecast performance to visit parameters. We emulate a generic hydrologic-hydrodynamic modelling cascade by imposing a bias and spatiotemporal correlations to the inflow error ensemble into the hydrodynamic domain. First, in agreement with previous research, estimation and correction for this bias leads to a clear improvement in keeping the forecast on track. Second, imagery obtained early in the flood is shown to have a large influence on forecast statistics. Revisit interval is most influential for early observations. The results are promising for the future of remote sensing-based water level observations for real-time flood forecasting in complex scenarios

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

A Through-the-Wall Radar Imaging Method Based on a Realistic Model

An image focusing method based on a realistic model for a wall is proposed for through-the-wall radar imaging using a multiple-input multiple-output array. A technique to estimate the wall parameters (i.e., position, thickness, and permittivity) from the radar returns is developed and tested. The estimated wall properties are used in the developed penetrating image formation to form images. The penetrating image formation developed is computationally efficient to realize real-time imaging, which does not depend on refraction points. The through-the-wall imaging method is validated on simulated and real data. It is shown that the proposed method provides high localization accuracy of targets concealed behind walls