Water is a major factor limiting cotton production and farmers must aim to optimise crop water use through timely irrigation scheduling decisions. Airborne video imagery when calibrated with a low density of ground based observations, offers the potential for near real time monitoring of crop condition, through sequential coverages of entire cotton fields. Using commercially available video equipment mounted on a light aircraft images were acquired of field experiments that were established in commercial cotton fields to test if the imagery could monitor changes in crop condition. Ground data collected from these experiments were used to evaluate green, red, near infrared and thermal band imagery for irrigated crop management. Prior to acquiring imagery, a ground radiometer study was conducted to investigate if canopy reflectance changed with the onset of crop water stress. Canopy reflectance decreased in the near infrared and green bands during the five day period prior to the crop's normal irrigation date. Red reflectance increased only after the crop irrigation was due, when the crop was suffering from water stress. The greatest change in canopy reflectance was in the near infrared region, attributable in part to a decrease in ground cover caused by canopy architectural changes including leaf wilting. The results of this experiment were used to select spectral filters for the video cameras. A range of crop conditions were identified in the imagery including; crop waterlogging, wheeltrack soil compaction, crop nitrogen status, different varieties, crop maturity, canopy development, soil moisture status, cotton yield and nutgrass weeds. Thermal imagery was the most successful for distinguishing differences in the crop soil moisture status. Near infrared imagery was most closely related to crop canopy development and is recommended for monitoring crop growth. Linear relationships were found between spectral responses in the imagery, crop reflectance (%) and crop temperature measured on the ground. Near infrared reflectance linearly increased, while spectral responses in the green, red and thermal bands exhibited an inverse relationship with plant height and ground cover. Imagery collected early in the season was affected by the soil background. Final lint yield was related to imagery in the red band. As the soil moisture level declined, crop temperature increased while reflectance in the green band decreased. To ensure an accurate relationship between soil moisture and thermal imagery, separate calibration equations are recommended for different stages in the season. Green, red and near infrared imagery were affected by the sun angle that caused one side of the imagery to appear brighter than the other. This problem was greatest in the green and red bands, but was not evident in the thermal imagery. Changes in solar radiation and air temperature on some occasions caused greater variation to the imagery between flights, than changes in crop condition per se. Therefore, it is not aIways possible to directly determine the soil moisture status from canopy temperature. Further research is required to correct imagery for environmental variables such as solar radiation, air temperature and vapour pressure deficit. Thermal imagery offers many improvements to current irrigation scheduling techniques including the facilitation of locating more representative ground sampling points. Thermal imagery also enables cotton fields on a farm to be ranked according to their soil moisture status. This then provides farmers with a visual picture of the crop water status across the whole farm, which is not possible using conventional ground scheduling techniques. At this stage, airborne video imagery will not replace soil moisture data collected for irrigation scheduling, however offers potential to enhance irrigation scheduling methods by addressing the problem of crop variability within cotton fields.

Anthropogenic influences and climate change are warming rivers in New Brunswick and threatening the cold water habitats of native salmonids. When ambient river temperatures in summer exceed the tolerance level of Atlantic salmon and brook trout, individuals behaviourally thermoregulate by seeking out cold water refugia. These critical thermal habitats are often created by tributaries and concentrated groundwater discharge. Thermal infrared imagery was used to map cold water anomalies along a 53 km reach of the Cains River on 23 July 2008. Although efficient and useful for mapping surface temperature of a continuous stream reach, the fish did not use all identified thermal anomalies as refugia. Overall, 100 % of observed large brook trout >35 cm in length were found in 30 % of the TIR-mapped cold water anomalies. Ninety eight percent of observed small brook trout 8 – 30 cm in length were found in 80 % of the mapped cold water anomalies and their densities within anomalies were significantly higher than densities outside of anomalies. Fifty nine percent of observed salmon parr were found in 65 % of the mapped anomalies; however, they were dispersed within study sites and their densities were not significantly different within anomalies compared to outside of the anomalies. No brook trout were observed at the seven noncold water study sites that were investigated. Preference curves for various habitat variables including velocity, temperature, depth, substrate, and deep water availability near cold water anomalies were developed based on field investigations during high temperature events (ambient river temperature >21 oC). Combined with thermal imagery, managers can use the physical descriptions of thermal refugia developed here as a tool to help conserve and restore critical thermal refugia for Atlantic salmon and brook trout on the Cains River, and potentially similar river systems.

This thesis examines the performance of some dim target detection algorithms in low-SNR imaging scenarios. In the past research, there have been numerous attempts for detection and tracking barely visible targets for military surveillance applications with infrared sensors. In this work, two of these algorithms are analyzed via extensive simulations. In one of these approaches, dynamic programming is exploited to coherently integrate the visible energy of dim targets over possible relative directions, whereas the other method is a Bayesian formulation for which the target likelihood is updated along time to be able to detect a target moving in any direction. Extensive experiments are conducted for these methods by using synthetic image sequences, as well as some real test data. The simulation results indicate that it is possible to detect dim targets in quite low-SNR conditions. Moreover, the performance might further increase, in case of incorporating any a priori information about the target trajectory.

With the increasing number and affordability of image capture devices, there is an increasing demand to objectively analyze and compare the quality of images. Image quality can also be used as an indicator to determine if the source image is of high enough quality to perform analysis on. When applied to real world scenarios, use of a blind algorithm is essential since a flawless reference image typically is unavailable. Recent research has shown promising results in no reference image quality utilizing natural scene statistics in the visual image space. Research has also shown that although the statistical profiles vary slightly, there are statistical regularities in IR images as well which would indicate that natural scene statistical models may be able to be applied. In this project, I will analyze BRISQUE quality features of IR images and determine if the algorithm can successfully be applied to IR images. Additionally, in order to validate the usefulness of these techniques, the BRISQUE quality features are analyzed using a detection algorithm to determine if they can be used to predict conditions which may cause missed detections. / text

Anthropogenic influences and climate change are warming rivers in New Brunswick and threatening the cold water habitats of native salmonids. When ambient river temperatures in summer exceed the tolerance level of Atlantic salmon and brook trout, individuals behaviourally thermoregulate by seeking out cold water refugia. These critical thermal habitats are often created by tributaries and concentrated groundwater discharge. Thermal infrared imagery was used to map cold water anomalies along a 53 km reach of the Cains River on 23 July 2008. Although efficient and useful for mapping surface temperature of a continuous stream reach, the fish did not use all identified thermal anomalies as refugia. Overall, 100 % of observed large brook trout >35 cm in length were found in 30 % of the TIR-mapped cold water anomalies. Ninety eight percent of observed small brook trout 8 – 30 cm in length were found in 80 % of the mapped cold water anomalies and their densities within anomalies were significantly higher than densities outside of anomalies. Fifty nine percent of observed salmon parr were found in 65 % of the mapped anomalies; however, they were dispersed within study sites and their densities were not significantly different within anomalies compared to outside of the anomalies. No brook trout were observed at the seven noncold water study sites that were investigated. Preference curves for various habitat variables including velocity, temperature, depth, substrate, and deep water availability near cold water anomalies were developed based on field investigations during high temperature events (ambient river temperature >21 oC). Combined with thermal imagery, managers can use the physical descriptions of thermal refugia developed here as a tool to help conserve and restore critical thermal refugia for Atlantic salmon and brook trout on the Cains River, and potentially similar river systems.

Master of Science / Department of Biological & Agricultural Engineering / Ajay Sharda / Discovered in 1971, sudden death syndrome (SDS), caused by the fungus Fusarium virguliforme, has spread from the US to South American and European countries. It has potential to infect soybean crops worldwide, causing yield losses of 10% to 15% and even 70% in extreme cases. There is a need for rapid spatial assessment of SDS. Currently, the extent and severity of SDS are scored using visual symptoms as indicators. This method can take hours to collect and is subject to human bias and changing environmental conditions. Color infrared (CIR) and thermal infrared (TIR) imagery detect changes in light reflectance (visible and near-infrared bands) and emittance (canopy temperature), respectively. Stressed crops may show deviations in light reflectiveness, as well as elevated canopy temperatures. The use of CIR and TIR imagery and flexible aerial remote sensing platforms offer an alternative for SDS detection and diagnosis compared to hand scoring methods. Crop stress and diseases have been detected using manned and unmanned aerial systems previously. Yet, to date, SDS has not been remotely assessed using CIR or TIR imagery collected with aerial platforms. The following research utilizes high throughput CIR and TIR imagery collected using a small unmanned aerial system (sUAS) to detect and assess SDS. A comparative evaluation of ground-based and aerial CIR methods for assessing SDS was conducted to understand the effectiveness of novel aerial SDS detection methods. Furthermore, a TIR case study investigating the use of potential thermal canopy changes for SDS detection was conducted to investigate the possibility of using TIR as an SDS indicator. CIR reflectance measured from a ground-based spectrometer and sUAS was collected data over a two-year period. Ground-based spectrometer data were collected weekly, while a sUAS collected aerial imagery late in the growing season each year before plant maturity. Pigment index (PI) values were derived from ground-based and aerial data. Results showed a strong negative correlation between SDS score and PI values. Aerial and ground-based data both showed strong correlations to SDS score, however, aerial data displayed a stronger relationship possibly due to minimal changes in environmental conditions. High SDS scores correlated strongly to aerial derived PI (R2 = 0.8359). Rapidly assessed high SDS allows for accurate screening of SDS critical for soybean breeding. The second year of the study investigated each component of SDS score, severity, and incidence. PI proved to have the best correlation with severity (R2 = 0.6313 and ρ = -0.8016) rather than incidence or SDS score. PI also correlated to SDS scores with R2 = 0.6159 and ρ = -0.7916. A sUAS mounted TIR camera collected imagery four times during the growing season when SDS foliar symptoms were just starting to appear. At the start of the study period, the correlation between canopy temperature and SDS is low (ρ = -0.2907), but increases over the growing season as SDS prevalence increases ending with a strong correlation (ρ = -0.7158). Early identification of SDS leads to the implementation of mitigation practices and changes in irrigation scheduling before the disease reaches severe symptoms. Early mitigation of SDS reduces yield loses for farmers. The use of both CIR and TIR aerial imagery captured using sUAS can provide rapid spatial assessments of SDS, which is required by both producers and plant breeders. PI derived from CIR imagery showing strong correlations to SDS score reinforce the idea of replacing the time-consuming traditional ground-based systems with the more flexible, faster, sUAS methods. TIR imagery was shown to be reliable in assessing SDS in soybeans further establishing another possible aerial method for early detection of SDS.

De nombreux travaux de recherche sont actuellement menés afin de rendre les caméras infrarouges plus compactes et moins chères. En infrarouge refroidi, le défi est de proposer un système cryogénique compact pouvant être intégré sur un système à faible capacité d’emport tel qu’un drone. Dans ce cadre, l’utilisation d’une lentille mince en remplacement du filtre froid du cryostat permettrait de limiter la masse supplémentaire à refroidir et de maintenir constant le temps de descente en froid. En infrarouge non refroidi, l’objectif est de concevoir un petit capteur infrarouge bas coût « grand public » que l’on pourra inviter dans nos maisons, nos voitures, voire nos smartphones. L’utilisation d’une lentille mince ouvrirait la voie à des imageurs infrarouges peu onéreux.Dans ce contexte, j’ai choisi d’étudier le comportement d’une lentille de Fresnel dite d’ordre élevé intégrée dans une configuration optique de type landscape lens. J’ai montré que cette architecture optique mince peut fonctionner sur une large bande spectrale et sur un grand champ de vue. Cependant, les lentilles de Fresnel d’ordre élevé étant mal modélisées dans la littérature, j’ai développé mes propres algorithmes de modélisation afin de prévoir les performances d’un tel système. Grâce à cette étude, j’ai ensuite proposé deux systèmes d’imagerie, l’un refroidi et l’autre non refroidi. Chacun des deux systèmes a fait l’objet d’un prototype et a été entièrement caractérisé expérimentalement. Les résultats expérimentaux obtenus m’ont permis de valider les performances anticipées théoriquement et de mettre en évidence un phénomène de chromatisme diffractif latéral. Ces systèmes ouvrent la voie à deux nouvelles générations de caméras infrarouges. J’ai montré que l’imageur infrarouge refroidi possède une qualité image satisfaisante pour des applications d’aide au pilotage. Le prototype non refroidi est lui entièrement compatible avec des applications domotiques. Il a suscité l’intérêt de différents acteurs industriels. / Miniaturizing infrared optical systems is a research area of great interest nowadays in order to make them lighter and cheaper. In the cooled infrared domain, the objective is to design a compact cryogenic camera that could be integrated in a small-capacity carrier like a drone. To that purpose, replacing the cold filter of the dewar by a thin lens would limit the cooled down mass and would stabilize the cool down time. In the uncooled infrared domain, the objective is to design a small general use camera at a low cost. Its use could be generalized in houses, cars or even smartphones. The use of a thin lens would also pave the way for low-cost infrared imagers. In this context, I chose to study the imagery properties of a high order Fresnel lens integrated in a landscape lens architecture. I have demonstrated that this architecture can be used within a wide spectral range and over a wide field of view. However, current optical design software perform poorly on high order Fresnel lenses. Therefore, I have developed my own algorithms to model the performances of such a system. With that study, I have been able to design two prototypes with their own objectives: the first one is cooled and the second one is uncooled. Both systems have been demonstrated and entirely characterized. The experiment results have validated the theoretical performances of the systems and they highlighted an original kind of lateral chromatic aberration.These two systems pave the way to two new generations of infrared cameras. Indeed, on one hand I have proved that the cooled infrared quality may be good enough to qualify for an aircraft piloting aid. On the other hand, the uncooled prototype is fully compatible with low cost surveillance applications and the system raised the interest of various companies.

Generic visual object tracking is the task of tracking one or several objects in all frames in a video, knowing only the location and size of the target in the initial frame. Visual tracking can be carried out in both the infrared and the visual spectrum simultaneously, this is known as multi-modal tracking. Utilizing both spectra can result in a more diverse tracker since visual tracking in infrared imagery makes it possible to detect objects even in poor visibility or in complete darkness. However, infrared imagery lacks the number of details that are present in visual images. A common method for visual tracking is to use discriminative correlation filters (DCF). These correlation filters are then used to detect an object in every frame of an image sequence. This thesis focuses on investigating aspects of a DCF based tracker, operating in the two different modalities, infrared and visual imagery. First, it was investigated whether the tracking benefits from using two channels instead of one and what happens to the tracking result if one of those channels is degraded by an external cause. It was also investigated if the addition of image features can further improve the tracking. The result shows that the tracking improves when using two channels instead of only using a single channel. It also shows that utilizing two channels is a good way to create a robust tracker, which is still able to perform even though one of the channels is degraded. Using deep features, extracted from a pre-trained convolutional neural network, was the image feature improving the tracking the most, although the implementation of the deep features made the tracking significantly slower.

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