Research has shown that people often fail to notice changes to visual scenes. This phenomenon is known as change blindness. This study investigated the effect of facial feature transformations on change blindness using change detection tasks involving a person as the object of change. 301 participants viewed a photo-story comprised of a few still frames. In the final frame, a selected facial feature of a character in the story was altered. Four different photo-stories were used, each utilising a different alteration. Questionnaires designed to determine whether the change was detected were administered. Results showed that changes to facial features considered to be more salient produced higher levels of change detection. A flicker test using the same images from the photo-story was administered to a further 75 participants and showed a similar pattern of results. It was concluded that in order to detect change, the changing stimuli have to be both salient and meaningful.

This paper presents Triangle-To-Cloud (T2C), a new approach for point cloud change detection. The method is compared to the established method Multiscale Model to Model Cloud Comparison (M3C2) on the accuracy to detect changes, from a point cloud to another. The comparison is performed on Light Detection and Ranging (LiDAR) mappings from an Ouster OS0-128 LiDAR sensor. Both T2C and M3C2 are tested with different parameters in all of the experiments conducted for evaluation. This work demonstrates in the experiments that T2C can outperform M3C2 in its ability to detect changes.

Dams are relatively simple hydraulic structures that provide vital services to communities in the United States (U.S.). However, many of the dams in the (U.S.) have surpassed their design life. Dams experience changes from external threats that result in deformation of the structure. Traditional surveying techniques provide limited information on deformation in pre-determined areas of a structure, but the collection effort can often be lengthy. In this research, different instruments used for change detection were reviewed and Terrestrial Laser Scanning (TLS), also known as ground-based Light Detection and Ranging (LiDAR), was selected as the most probable method to accurately evaluate deformation in dams. TLS is a remote sensing instrument that uses light to form a pulsed laser to measure ranges to variable targets, and it provides the ability to measure displacement with high accuracy using dense point clouds collected in a short amount of time. Deformation is identified by measuring changes in point clouds generated by TLS. The accuracy of TLS to identify deformation was tested on a thin arch dam at the Big Black Test site in Vicksburg, Mississippi, using the TLS system, Terrestrial Laser Scanner RIEGL VZ-400, for data collection and for registering scan positons between a pre-test condition and a post-test condition. Final data analysis was performed using Microstation TopoDOTTM Wall Monitoring Tool.

In the past 40 years, floods have become a bane of Mozambique’s inhabitants and economy. The latest of them, caused by the cyclone Idai, has devastated the area resulting in loss of life and property. It was estimated that around 715 000 hectares of farmland was destroyed as a result of the cyclone. The main goal of this thesis was to assess the extent of the flooding and to determine the types of land cover that were affected. This was done in Google Earth Engine, using SAR change detection on Sentinel 1 data to create a mask for the flooded areas, followed by a supervised image classification on Sentinel 2 data to identify the types of land cover that were flooded. Two classifications were done, using imagery from early periods of the country’s plant growing season and later periods of the same season, respectively. The results of both classifications were below standard, with the main problems stemming from difficulties with differentiating between agriculture and roads along with agriculture and vegetation. Multiple ways to improve the results and avoid the errors in future similar projects were discussed, including using multi temporal data and utilizing a road map for the area to create a large amount of training points for the classification. In conclusion, while the results were not as good as was envisioned, the thesis provided ample opportunity to analyze errors and to theorize methods for improving future work.

A commercial-off-the-shelf Ku-band Frequency Modulated Continuous Wave (FMCW) synthetic aperture radar (SAR) system is coupled with a custom built two-dimensional scanning system. This system is installed in an alpine environment and pointed at a snow-unstable mountain slope for the duration of a Utah winter. The radar scanning system, designed to be capable of mapping a snowpack and its layers, is employed to create a series of three-dimensional images from a remote location. Individual images demonstrate the ability to directly detect snow layers, Furthermore, successive images are compared to track volume magnitude and phase values over the course of winter, including many snow deposition and melt events. The digital signal processing techniques used to create a high-resolution voxel (a three-dimensional pixel) map describing these snow layers is discussed. Results are discussed and further work is suggested for improving upon the results of this work.

Research on visual working memory (VWM) suggests a capacity limit of three to four objects (Luck & Vogel, 1997), but recent studies on the fidelity of VWM capacity for objects indicates that informational bandwidth, which can vary with factors like complexity and amenability to perceptual grouping, can interact with this capacity (Brady, Konkle & Alvarez, 2011). For example, individual features can be grouped into objects for an added benefit in VWM capacity (Xu, 2002). Along these lines, the Gestalt principles of proximity and connectedness have been shown to benefit VWM, although they do not influence capacity equally (Xu 2006; Woodman, Vecera & Luck, 2003). Closure, which has not been investigated for its influence in VWM capacity, is similar to connectedness and proximity as it promotes the perception of a coherent object without physical connections. In the current experiment, we evaluated whether closure produces similar or greater VWM capacity advantages compared to proximity by having participants engage in a change detection task. Four L-shaped features were grouped in tilted clusters to either form an object (closure condition) or not (no-object condition), with a set size of two (8 L features), four (16 L features), or six clusters (24 L features). Following a brief mask (1000 ms), the orientation of one cluster was changed (tilted 25 or -25 degrees) on half the trials. Our results indicate that there was no difference in accuracy or reaction time for the perceptual grouping conditions of closure/no-object, although we did find a main effect for set size and change conditions. Overall, it seems that grouping by closure provides no further advantages to VWM capacity than proximity; however, more experiments need to be conducted to solidify the findings of the current experiment.

Coastal Louisiana U.S. is facing wetland loss caused mainly by geologic subsidence and sea-level rise. These losses are accelerated by human activities such as the creation of canals and waterways for gas and oil extraction. Wetland loss in coastal Louisiana has encouraged to the implementation of various wetland restoration techniques. Marsh terraces are a one restoration technique consisting of segmented berms of soil that are built in inland coastal ponds. They are designed to increase marsh area, dissipate wind driven waves, encourage marsh expansion, and possibly reduce shoreline erosion. Marsh terraces have been implemented for almost 30 years; however, little research has been conducted to determine their effectiveness at reducing wave energy. Therefore, the overall goal of this research is to find the most optimal terrace design at reducing significant wave height, and therefore wave energy. The specific objectives of this study are to 1) assess terrace performance and longevity over time, 2) simulate wave climates in marsh terrace sites and determine the effectiveness of marsh terraces for the reduction of wave energy, and 3) assess the effectiveness of different terrace designs at reducing significant wave height during low winds and cold front passages in coastal Louisiana. These objectives were accomplished through remote sensing and numerical wave modeling. This study found that there was more predominant deposition than erosion in 20 marsh terrace fields. The study also used a numerical model to simulate small, high frequency waves in two terrace sites, finding an agreement between modeled and observed data. Moreover, wave height was reduced in terrace sites compared to unterraced sites. Finally, it was found that the chevron design is the most optimal terrace design at reducing significant wave height in a variety of wind conditions. This study adds to our knowledge of marsh terrace performance. In this way, marsh terraces may be used as an effective restoration technique at reducing wave energy, not just in Louisiana, but throughout the Gulf Coast, the U.S., and other coasts worldwide that are facing wetland loss.

The method for developing a soil moisture inversion algorithm using Radar data can be approached in two ways: the multiple-incident angle approach and the change detection method. This thesis discusses how these two methods can be used to predict surface soil moisture. In the multiple incident angle approach, surface roughness can be mapped, if multiple incident angle viewing is possible and if the surface roughness is assumed constant during data acquisitions. A backpropagation neural network (NN) is trained with the data set generated by the Integral Equation Method (IEM) model. The training data set includes possible combinations of backscatter obtained as a result of variation in dielectric constant within the period of data acquisitions. The inputs to the network are backscatter acquired at different incident angles. The outputs are correlation length and root mean square height (rms). Once the roughness is mapped using these outputs, dielectric constant can be determined. Three different data sets, (backscatter acquired from multiplerequencies, multiple-polarizations, and multiple-incident angles) are used to train the NN. The performance of the NN trained by the different data sets is compared. The next approach is the application of the change detection concept. In this approach, the relative change in dielectric constant over two different periods is determined from Radarsat data using a simplified algorithm. The vegetation backscatter contribution can be removed with the aid of multi-spectral data provided by Landsat. A method is proposed that minimizes the effect of incident angle on Radar backscatter by normalizing the acquired SAR images to a reference angle. A quantitative comparison of some of the existing soil moisture estimation algorithms is also made

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