Thesis (M. Eng.), Memorial University of Newfoundland, 1998. / Bibliography: leaves 117-125.

Title from first page of PDF document. Includes bibliographical references (p. 40).

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2005. / Thesis Advisor(s): Michael A. Morgan, Thayananthan Thayaparan. Includes bibliographical references (p. 119-120). Also available online.

Thesis (M.Eng.)--Memorial University of Newfoundland, 2004. / Bibliography: leaves 134-138.

Tropical forests are extremely important ecosystems which play a substantial role in the global carbon budget and are increasingly dominated by anthropogenic disturbance through deforestation and forest degradation, contributing to emissions of greenhouse gases to the atmosphere. There is an urgent need for forest monitoring over extensive and inaccessible tropical forest which can be best accomplished using spaceborne satellite data. Currently, two key processes are extremely challenging to monitor: forest degradation and post-disturbance re-growth. The thesis work focuses on these key processes by considering change indicators derived from radar remote sensing signal that arise from changes in forest structure. The problem is tackled by exploiting spaceborne Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) observations, which can provide forest structural information while simultaneously being able to collect data independently of cloud cover, haze and daylight conditions which is a great advantage over the tropics. The main principle of the work is that a connection can be established between the forest structure distribution in space and signal variation (spatial statistics) within backscatter and Digital Surface Models (DSMs) provided by SAR. In turn, forest structure spatial characteristics and changes are used to map forest condition (intact or degraded) or disturbance. The innovative approach focuses on looking for textural patterns (and their changes) in radar observations, then connecting these patterns to the forest state through supporting evidence from expert knowledge and auxiliary remote sensing observations (e.g. high resolution optical, aerial photography or LiDAR). These patterns are descriptors of the forest structural characteristics in a statistical sense, but are not estimates of physical properties, such as above-ground biomass or canopy height. The thesis tests and develops methods using novel remote sensing technology (e.g. single-pass spaceborne InSAR) and modern image statistical analysis methods (wavelet-based space-scale analysis). The work is developed on an experimental basis and articulated in three test cases, each addressing a particular observational setting, analytical method and thematic context. The first paper deals with textural backscatter patterns (C-band ENVISAT ASAR and L-band ALOS PALSAR) in semi-deciduous closed forest in Cameroon. Analysis concludes that intact forest and degraded forest (arising from selective logging) are significantly different based on canopy structural properties when measured by wavelet based space-scale analysis. In this case, C-band data are more effective than longer wavelength L-band data. Such a result could be explained by the lower wave penetration into the forest volume at shorter wavelength, with the mechanism driving the differences between the two forest states arising from upper canopy heterogeneity. In the second paper, wavelet based space-scale analysis is also used to provide information on upper canopy structure. A DSM derived from TanDEM-X acquired in 2014 was used to discriminate primary lowland Dipterocarp forest, secondary forest, mixed-scrub and grassland in the Sungai Wain Protection Forest (East Kalimantan, Indonesian Borneo) which was affected by the 1997/1998 El Niño Southern Oscillation (ENSO). The Jeffries- Matusita separability of wavelet spectral measures of InSAR DSMs between primary and secondary forest was in some cases comparable to results achieved by high resolution LiDAR data. The third test case introduces a temporal component, with change detection aimed at detecting forest structure changes provided by differencing TanDEM-X DSMs acquired at two dates separated by one year (2012-2013) in the Republic of Congo. The method enables cancelling out the component due to terrain elevation which is constant between the two dates, and therefore the signal related to the forest structure change is provided. Object-based change detection successfully mapped a gradient of forest volume loss (deforestation/forest degradation) and forest volume gain (post-disturbance re-growth). Results indicate that the combination of InSAR observations and wavelet based space-scale analysis is the most promising way to measure differences in forest structure arising from forest fires. Equally, the process of forest degradation due to shifting cultivation and post-disturbance re-growth can be best detected using multiple InSAR observations. From the experiments conducted, single-pass InSAR appears to be the most promising remote sensing technology to detect forest structure changes, as it provides three-dimensional information and with no temporal decorrelation. This type of information is not available in optical remote sensing and only partially available (through a 2D mapping) in SAR backscatter. It is advised that future research or operational endeavours aimed at mapping and monitoring forest degradation/regrowth should take advantage of the only currently available high resolution spaceborne single-pass InSAR mission (TanDEM-X). Moreover, the results contribute to increase knowledge related to the role of SAR and InSAR for monitoring degraded forest and tracking the process of forest degradation which is a priority but still highly challenging to detect. In the future the techniques developed in the thesis work could be used to some extent to support REDD+ initiatives.

Indoor localization is the process of localizing people or objects inside a building in the same way GPS does in an outside environment. In recent years, researchers have successfully achieved improvement in indoor localization accuracy. Still there are many limitations to overcome in performing and achieving good accuracy in indoor localization. The interest in estimating the location of something inside a building with good accuracy is very strong. In this thesis we first propose an indoor localization technique relative to Wi-Fi access points along with a novel heuristic search based algorithm, named MuSLoc. Through simulation and comparative studies, we have shown that MuSLoc outperforms other indoor localization models without the help of fingerprinting or crowdsourcing about the environment. MuSLoc provides almost the same accuracy in LOS (Line of Sight) and NLOS (Non-Line of Sight) environments with regular infrastructure that has recently been provided by smart phones. This model doesn't require any additional hardware support in order to perform well. Further, we propose another indoor localization based Wi-Fi device tracker model, named MSTracker, which is able to track both moving and non-moving devices inside a building. This model is also free from specialized infrastructure and can perform well without any training data information. Through real time simulation and analysis we have shown that it performs more accurately than other available models. Through extensive simulations in a real time environment and analysis of performance comparatives with other available models, we have shown that both MuSLoc and MSTracker perform more accurately with COTS than any other method of indoor localization and tracking of objects inside a building. The complete package of MuSLoc and MSTracker can perform perfectly with recently available Wi-Fi modules and smartphones.

This thesis investigates the viability of applying a shape-from-shading technique to SAR imagery. A shape-from-shading algorithm is derived and tested on a single site for which both a Seasat SAR image and Digitial Elevation Model (DEM) were available. The shape-from-shading technique used in this thesis follows an approach proposed by Frankot and Chellappa for processing slant range SAR imagery. The algorithm incorporates a one-step technique for projecting non-integrable surface orientation estimates onto an integrable set in the frequency domain along with the iterative convergent shape-from-shading algorithm of Brooks and Horn. The significant issues and choices made in implementing the shape-from-shading algorithm and in preparing the SAR data and DEM are discussed. The shape-from-shading algorithm was applied to both the test site SAR image and images synthesized from the DEM. Reflectance models were derived from the SAR image and DEM. By quantitatively comparing the shape-from-shading results with the initial conditions used for the experiments, it was found that the algorithm produced substantially better results when applied to the synthesized images; however, when applied to the SAR image, there was no significant improvement over the initial conditions. / Science, Faculty of / Computer Science, Department of / Graduate

Synthetic Aperture Focusing Technique (SAFT) is a powerful method to createfocused images of the inside of opaque samples by using delay-and-sum of acquireddata. It gives a high resolution and when using a generation laser and a detectionlaser it is also non-contact. This thesis was made at Swerim, and the aim wasto create an 3D-SAFT algorithm and to visualise the reconstructed image. Twosamples were used, both were 3D-printed with known defects that varied in sizefrom 0.05 mm to 1 mm. The defects were lined up in rows, with 10 in each row.After the algorithm was used on the acquired data from the two samples, six toeight defects were found in each row. Both samples had three rows of defects. Themeasured sizes of the defects were not exactly as the actual size but ranged a fewmillimetre too small or big compared to the real size. Overall the algorithm workswell. The resolution of the 3D images are the same as for the 2D-SAFT algorithmalready made by Swerim. As of now the 3D images may not be worth the time ittakes to process, but if a better way to visualise the data is made in the future, itwill be good to be able to see the defects in 3D.

Noise and degradation reduction is of significant importance in virtually all systems where these phenomena are present, specifically in the fields of signal and image processing.  The effect of image processing on target detection is of significant interest because noise and degradations can greatly reduce the effectiveness of detection algorithms, due to the presence of high intensity noise which is often mistaken as a target.  In signal processing, noise in vibration data, or any time-series data, can reduce the accuracy of measurement and can prevent the passing of useful information. Many filters that have been developed are designed to reduce a single class of noise, such as Wiener and Frost filters.  When these filters are applied to types of noise that they were not designed for, the effect of the noise reduction can be greatly reduced.  The proposed Two-Stage Non-Adaptive Convolution (TSNAC) filter significantly reduces both additive and multiplicative noise in these two unique systems. The performance of these filters is compared through several Image Quality (IQ) metrics. It will be shown that the proposed TSNAC filter reduces noise and degradations more effectively in both SAR images and synthetic vibration data than the competing filters.  It will show higher IQ scores, greater computational efficiency in target detection, and significant improvement in signal restoration of simulated vibration data. / Master of Science

The objectives of this research are to support the development of state-of-the-art methods using remotely sensed data to detect slides or anomalies in an efficient and cost-effective manner based on the use of SAR technology. Slough or slump slides are slope failures along a levee, which leave areas of the levee vulnerable to seepage and failure during high water events. This work investigates the facility of detecting the slough slides on an earthen levee with different types of polarimetric Synthetic Aperture Radar (polSAR) imagery. The source SAR imagery is fully quad-polarimetric L-band data from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area encompasses a portion of the levees of the lower Mississippi river, located in Mississippi, United States. The obtained classification results reveal that the polSAR data unsupervised classification with features extraction produces more appropriate results than the unsupervised classification with no features extraction. Obviously, supervised classification methods provide better classification results compared to the unsupervised methods. The anomaly identification is good with these results and was improved with the use of a majority filter. The classification accuracy is further improved with a morphology filter. The classification accuracy is significantly improved with the use of GLCM features. The classification results obtained for all three cases (magnitude, phase, and complex data), with classification accuracies for the complex data being higher, indicate that the use of synthetic aperture radar in combination with remote sensing imagery can effectively detect anomalies or slides on an earthen levee. For all the three samples it consistently shows that the accuracies for the complex data are higher when compared to those from the magnitude and phase data alone. The tests comparing complex data features to magnitude and phase data alone, and full complex data, and use of post-processing filter, all had very high accuracy. Hence we included more test samples to validate and distinguish results.