This is a master thesis of the Master of Science degree program in Applied Physics and Electrical Engineering (Y) at Linköping University. The goal of the projectis to develop an application for creating a map in real time from a video camera on a miniature unmanned aerial vehicle. This thesis project and report is a first exploratory study for this application. It implements a prototype method and evaluates it on sample sequences from an on-board video camera. The method first looks for good points to follow in the image and then tracks them in a sequence.The image is then pasted, or merged, together with previous images so that points from the different images align. Two methods to find good points to follow are examined with focus on real-time performance. The result is that the much faster FAST detector method yielded satisfactory results good enough to replace the slower standard method of the Harris-Stephens corner detector. It is also examined whether it is possible to assume that the ground is a flat surface in this application or if a computationally more expensive method estimating altitude information has to be used. The result is that at high altitudes or when the ground is close to flat in reality and the camera points straight downwards a two-dimensional method will do. If flying lower or with high objects in the picture, which is often the case in this application, it must to be taken into account that the points really are at different heights, hence the ground can not be assumed to be flat.

Hurricane Isaac’s landfall on the coast of Louisiana spawned a hydrological research project between Mississippi State University (MSU), the Northern Gulf Institute (NGI), and the National Oceanic and Atmospheric Administration (NOAA) in the Lower Pearl River Basin (LPRB). Unmanned aerial systems data collection missions were scheduled every two months in the LPRB. This research provides a comparison between Landsat-8 imagery and corresponding UAS imagery with regards to the four remote sensing resolutions: spatial, spectral, radiometric, and temporal. Near-infrared (NIR) imagery from each platform was compared by land-water masks and statistical comparisons. A classification method known as natural breaks with Jenks Optimization determined threshold values between land and water for each image. Land-water masks revealed substantial differences between areas of land and water in comparing imagery. The overall difference in average land and water percentages between the two platforms was 1.77%; however, a larger percentage was 20.41% in a single comparison.

Multi-spectral imagery provides a robust and low-cost dataset for assessing wetland extent and quality over broad regions and is frequently used for wetland inventories. However in forested wetlands, hydrology is obscured by tree canopy making it difficult to detect with multi-spectral imagery alone. Because of this, classification of forested wetlands often includes greater errors than that of other wetlands types. Elevation and terrain derivatives have been shown to be useful for modelling wetland hydrology. But, few studies have addressed the use of LiDAR intensity data detecting hydrology in forested wetlands. Due the tendency of LiDAR signal to be attenuated by water, this research proposed the fusion of LiDAR intensity data with LiDAR elevation, terrain data, and aerial imagery, for the detection of forested wetland hydrology. We examined the utility of LiDAR intensity data and determined whether the fusion of Lidar derived data with multispectral imagery increased the accuracy of forested wetland classification compared with a classification performed with only multi-spectral image. Four classifications were performed: Classification A - All Imagery, Classification B - All LiDAR, Classification C - LiDAR without Intensity, and Classification D - Fusion of All Data. These classifications were performed using random forest and each resulted in a 3-foot resolution thematic raster of forested upland and forested wetland locations in Vermilion County, Illinois. The accuracies of these classifications were compared using Kappa Coefficient of Agreement. Importance statistics produced within the random forest classifier were evaluated in order to understand the contribution of individual datasets. Classification D, which used the fusion of LiDAR and multi-spectral imagery as input variables, had moderate to strong agreement between reference data and classification results. It was found that Classification A performed using all the LiDAR data and its derivatives (intensity, elevation, slope, aspect, curvatures, and Topographic Wetness Index) was the most accurate classification with Kappa: 78.04%, indicating moderate to strong agreement. However, Classification C, performed with LiDAR derivative without intensity data had less agreement than would be expected by chance, indicating that LiDAR contributed significantly to the accuracy of Classification B.

Submitted by GILMAR RENAN KISAKI OLIVEIRA null (renan.kisaki@gmail.com) on 2016-12-20T03:09:27Z No. of bitstreams: 1 2016_MSc_Gilmar_Renan_Unesp.pdf: 3787156 bytes, checksum: 03bf116759b5ae85b2e5302f167eb985 (MD5) / Approved for entry into archive by Felipe Augusto Arakaki (arakaki@reitoria.unesp.br) on 2016-12-21T19:54:13Z (GMT) No. of bitstreams: 1 oliveira_grk_me_prud.pdf: 3787156 bytes, checksum: 03bf116759b5ae85b2e5302f167eb985 (MD5) / Made available in DSpace on 2016-12-21T19:54:13Z (GMT). No. of bitstreams: 1 oliveira_grk_me_prud.pdf: 3787156 bytes, checksum: 03bf116759b5ae85b2e5302f167eb985 (MD5) Previous issue date: 2016-02-29 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Esta dissertação contempla o desenvolvimento de um método que combina os dados LiDAR (Light Detection And Ranging) obtidos por sistema de varredura LASER (Light Amplification by Stimulated Emission of Radiation) e imagens aéreas de uma mesma região a fim de extrair os contornos de telhados de edificações, onde os parâmetros de orientação das imagens são conhecidos. O método proposto neste trabalho pode ser dividido nas seguintes etapas: extração das edificações nos dados LiDAR; extração dos contornos das edificações nos dados LiDAR; e refinamento dos contornos das edificações integrando dados LiDAR e imagens. Primeiramente, as edificações são extraídas dos dados LiDAR, seguida da determinação dos seus pontos de contorno, que por sua vez, são projetados em duas imagens que formam um modelo estereoscópico. Às imagens do par é aplicado o algoritmo de detecção de bordas de Canny com o objetivo de identificar as bordas de edificações. Tendo os contornos dos telhados de edificações (provenientes dos dados LiDAR) projetados nas imagens de bordas, é realizado um procedimento de busca dos pontos de bordas de edificações nas imagens. Com base nos pixels identificados como bordas de edificações e com o propósito de obter uma figura geométrica que represente os contornos, é aplicado o ajuste de retas 2D pelo Método dos Mínimos Quadrados (MMQ) integrado à filtragem de pontos espúrios por meio do teste Tau. Para avaliar o método proposto e implementado foram utilizados dados LiDAR com densidade média de 6,7 pontos/m² e imagens aéreas digitais com GSD de 8 cm. Os resultados obtidos na avaliação dos experimentos mostraram que o método proposto conseguiu extrair os contornos dos telhados, com melhores resultados para edificações isoladas que não possuíam projeção de sombras ou objetos sobre elas, atingindo valores da ordem de 0,97 GSD e 1,80 GSD, para o REMQ em planimetria e altimetria, respectivamente. / This dissertation proposes a methodology to extract building boundary through the integration of LiDAR data and aerial imagery where the image orientation parameters are known. The proposed method can be divided into following steps: building extraction from LiDAR data; building boundary extraction from LiDAR data; and refinement of building boundary through the integration of LiDAR data and optical imaging. Building are first extracted from LiDAR data, then building boundaries are determined in LiDAR data and projected onto the stereo pair of aerial images. These aerial images are results from the application of Canny edge detector in order to identify building boundaries from images. Since the 3D building boundaries (determined from LiDAR data) are projected onto the Canny images, a search mechanism is performed to find the building edge points in these images. A 2D line adjustment by Least Squares Method (LSM) is performed, followed by outlier detection based on Tau Statistical Test, for generating a geometric shape to represent the buildings through the building edge pixels identified. In order to evaluate the proposed approach, LiDAR data with approximate density of 6.7 pts/m² and digital aerial images with GSD around 8 cm were used. The results showed that the proposed method enabled to extract building roof boundaries with best results for isolated buildings without objects or shadow’s projection on them with the root mean square error (RMSE) around 0.97 GSD and 1.80 GSD in planimetry and altimetry, respectively. / CNPq: 130473/2013-8

This thesis explores the genetic architecture of malting quality within the Virginia Tech barley breeding program, and discusses implications for imposing selection on complex traits that are difficult to phenotype. Malting quality measures are destructive, and can not be performed before selection must be made for advancement of breeding lines in winter barley. A growing body of evidence suggests that malt quality is influenced by malting regime, growing environment, line genotype, and the interactions between them. We aim to better understand the genetic effect on malt quality in two manners: first, as it relates to the genetic architecture regulating malt quality parameters, and second the relationship between genetic growth patterns to end-use malting traits. This study included two years of breeding trial data of two and six-row winter malt barley across two locations. Results of a genome-wide association scan and genomic prediction of malt quality traits indicated that they are largely quantitative traits with complex inheritance. Previous studies have identified quantitative trait loci and genes regulating malt quality traits in markedly different germplasm. Heritability of traits ranged from 0.27 to 0.72, while mean predictive abilities ranged from 0.45 to 0.74. Thus, selection on genomic estimated breeding values (gEBVs) should perform similarly to selection on single phenotypic observations of quality, but can be done within the same season. This indicates that genomic selection may be a viable method to accelerate genetic improvement of malting quality traits. The use of gEBVs requires that lines be genotyped with genome-wide markers, somewhat limiting the number of candidate individuals. Selection on growth and development traits genetically correlated with quality measures could allow for selection among a much greater number of candidates if high-throughput phenotypes can be collected on many ungenotyped indivduals. Growth and development was quantified by the near-infrared vegetation index (NDVI) extracted from aerial images captured from multiple time points throughout the growing season. Estimates of genetic correlation identified time points throughout the season when quality traits are related to growth and development. We demonstrated that aerial imagery can discern growth patterns in barley and suggest ways it can be incorporated into the breeding pipeline. / Master of Science / Malt barley (Hordeum vulgare) is the preferred source of fermentable sugar used to brew beer. Currently, the majority of malt barley used in the United States is grown in the upper mid-west or imported from Europe. The east coast could become a producing region if high quality, disease resistant varieties were available to growers. The Virginia Tech small grains breeding program began breeding locally adapted malt barley in 2010. This project aims to improve the breeding process by incorporating information from genomic sequencing, malt quality and aerial imagery. Malt barley differs from that used for animal feed or human food because specific quantities of starches, proteins, and enzymes are necessary in the brewing process. The quantity of these molecules are determined through lab analysis and determine the grain's suitability for particular brewing styles. This analysis is timeconsuming and costly because it involves a three-step process of malting the grain, brewing with the malt, and analyzing the wort. The wort is the liquid sugar solution which is produced by heating the malt with water to a high temperature in a process called 'mashing'. Lab quality analysis for the thousands of lines evaluated in a breeding program in any given year is unfeasible. However, by understanding the genetic regulation of malt quality traits, breeders can employ techniques like genomic selection to improve these traits in a shorter amount of time. Additionally, this work identifies relationships between growth and quality. The grain is the result of the plant's growth throughout the entirety of the season. Measuring growth repeatedly through time was previously difficult until the advent of aerial imagery. Images captured from drones have been used to quantify growth in a variety of plants, but is not extensively done in malt barley. Relating growth to quality will help breeders understand genetic patterns of growth and development which may be advantageous in the production of high quality malt barley.

Compression of data has been historically always important; more data is gettingproduced and therefore has to be stored. While hardware technology advances,compression should be a must to reduce storage occupied and to keep the data intransmission as small as possible. Set redundancy has been developed in 1996 but has since then not received a lot ofattention in research. This paper tries to implement two set redundancy methods –the Max-Min-Predictive II and also the Intensity Mapping algorithm to see if thismethod could be used on large scale aerial imagery in the geodata field. After using the set redundancy methods, different individual image compressionmethods were applied and compared to the standard JPEG2000 in lossless mode.These compression algorithms were Huffman, LZW, and JPEG2000 itself. The data sets used were two images each taken from 2019, one pair with 60% overlap,the other with 80% overlap. Individual compression of images is still offering abetter compression ratio, but the set redundancy method produces results which areworth investigating further with more images in a set of similar images. This points to future work of compressing a larger set with more overlap and moreimages, which for greater potential matching should be overlaid more carefully toensure matching pixel values. / Datakomprimering har historiskt alltid varit viktigt; mer data än någonsin producerasoch behöver lagras. Trots teknologiska framsteg inom lagrings- och datateknologierär komprimering ett måste för att reducera mängden lagring som krävs och underlättavid överföringar genom att mindre filmängd måste skickas. Set redundancy utvecklades 1996, men har sedan dess inte fått så mycket uppmärksamhetinom forskning. Det här pappret försöker implementera två olika set redundancy-metoder – Max-Min-Predictive II och Intensity Mapping algoritmen, för att se omdenna metod kan användas på flygbilder från storskalig flygbildsinsamling. Efter användandet av set redundancy metoder på ett set av flygbilder, utnyttjadesandra bildkomprimeringsmetoder för enskilda bilder på resultatet, detta jämfördesmed den icke-förstörande JPEG2000 komprimeringen av originalbilderna. Komprimeringsalgoritmernasom användes på set redundancy-resultatet var Huffman, LZW,och JPEG2000. Det dataset som användes bestod av två par av bilder från 2019, där en hade överlapppå 60% och det andra paret på 80%. Individuell komprimering av dataseten gaven högre komprimeringsgrad än set redundancy metoder, men set redundancy har enskalningspotential när fler bilder läggs till i ett set, vilket är värt att undersöka vidare. Detta pekar på framtida arbeten där komprimering av större dataset med högreöverlapp mellan bilder, som med en högre geografisk korrekthet läses in ovanpåvarandra, kan testas.

Remote sensing techniques are important for detecting disease within the turfgrass canopy. Herein, we look at two such techniques to assess their viability in detecting and isolating turfgrass diseases. First, thermal imagery is used to detect differences in canopy temperature associated with the onset of brown patch infection in tall fescue. Sixty-four newly seeded stands of tall fescue were arranged in a randomized block design with two runs with eight blocks each containing four inoculum concentrations within a greenhouse. Daily measurements were taken of the canopy and ambient temperature with a thermal camera. After five consecutive days differences were detected in canopy – ambient temperature in both runs (p=0.0015), which continued for the remainder of the experiment. Moreover, analysis of true colour imagery during this time yielded no significant differences between groups. A field study comparing canopy temperature of adjacent symptomatic and asymptomatic tall fescue and creeping bentgrass canopies showed differences as well (p<0.0492). The second project attempted to isolate spring dead spot from aerial imagery of bermudagrass golf course fairways using a Python script. Aerial images from unmanned aerial vehicle flights were collected from four fairways at Nicklaus Course of Bay Creek Resort in Cape Charles, VA. Accuracy of the code was measured by creating buffer zones around code generated points and measuring how many disease centers measured by hand were eclipsed. Accuracies measured as high as 97% while reducing coverage of the fairway by over 30% compared to broadcast applications. Point density maps of the hand and code points also appeared similar. These data provide evidence for new opportunities in remote turfgrass disease detection. / Master of Science in Life Sciences / Turfgrasses are ubiquitous, from home lawns to sports fields, where they are used for their durability and aesthetics. Disease within the turfgrass canopy can ruin these aspects of the turfgrass reducing its overall quality. This makes detection and management of disease within the canopy an important part of maintaining turfgrass. Here we look at the effectiveness of imaging techniques in detecting and isolating disease within cool-season and warm-season turfgrasses. We test the capacity for thermal imagery to detect the infection of tall fescue (Festuca arundenacea) with Rhizoctonia solani, the causal agent of brown patch. In greenhouse experiments, differences were detected in normalized canopy temperature between differing inoculation levels at five days post inoculation, and in field conditions we were able to observe differences in canopy temperature between adjacent symptomatic and non-symptomatic stands. We also developed a Python script to automatically identify and record the location of spring dead spot damage within mosaicked images of bermudagrass golf fairways captured via unmanned aerial vehicle. The developed script primarily used Hough transform to mark the circular patches within the fairway and recorded the GPS coordinates of each disease center. When compared to disease incidence maps created manually the script was able to achieve accuracies as high as 97% while reducing coverage of the fairway by over 30% compared to broadcast applications. Point density maps created from points in the code appeared to match those created manually. Both findings have the potential to be used as tools to help turfgrass managers.

We present a statistical model of recreational trails, and a method to infer trail routes from geophysical data, namely aerial imagery and terrain elevation. We learn a set of textures (textons) that characterize the imagery, and use the textons to segment each image into super-pixels. We also model each texton's probability of generating trail pixels, and the direction of such trails. From terrain elevation, we model the magnitude and direction of terrain gradient on-trail and off-trail. These models lead to a likelihood function for image and elevation. Consistent with Bayesian reasoning, we combine the likelihood with a prior model of trail length and smoothness, yielding a posterior distribution for trails, given an image. We search for good values of this posterior using both a novel stochastic variation of Dijkstra's algorithm, and an MCMC-inspired sampler. Our experiments, on trail images and groundtruth collected in the western continental USA, show substantial improvement over those of the previous best trail-finding methods.

This work has aimed to implement an easy-to-use application which can be used to navigate through aerial imagery, assign sections of this image for different classes. Based on these category assignments the application can autonomously assign categories to so-far unknown fields, hence it helps the user in further classification. The output of the application is an index file, which can serve as underlying dataset for further analysis of a given area from geographic or economic point-of-view. To fulfil this task the program uses standard MPEG-7 descriptors to perform the feature extraction upon which the classification relies.

Aircraft-mounted cameras have potential to greatly increase the effectiveness of wilderness search and rescue efforts by collecting photographs or video of the search area. The more data that is collected, the more difficult it becomes to process it by visual inspection alone. This work presents a method for automatically detecting unusual objects in aerial video to assist people in locating signs of missing persons in wilderness areas. The detector presented here makes use of anomaly detection methods originally designed for hyperspectral imagery. Multiple anomaly detection methods are considered, implemented, and evaluated. These anomalies are then aggregated into spatiotemporal objects by using the video's inherent spatial and temporal redundancy. The results are therefore summarized into a list of unusual objects to enhance the search technician's video review interface. In the user study reported here, unusual objects found by the detector were overlaid on the video during review. This increased participants' ability to find relevant objects in a simulated search without significantly affecting the rate of false detection. Other effects and possible ways to improve the user interface are also discussed.