Using Linear Features for Aerial Image Sequence Mosaiking

With recent advances in sensor technology and digital image processing techniques, automatic image mosaicking has received increased attention in a variety of geospatial applications, ranging from panorama generation and video surveillance to image based rendering. The geometric transformation used to link images in a mosaic is the subject of image orientation, a fundamental photogrammetric task that represents a major research area in digital image analysis. It involves the determination of the parameters that express the location and pose of a camera at the time it captured an image. In aerial applications the typical parameters comprise two translations (along the x and y coordinates) and one rotation (rotation about the z axis). Orientation typically proceeds by extracting from an image control points, i.e. points with known coordinates. Salient points such as road intersections, and building corners are commonly used to perform this task. However, such points may contain minimal information other than their radiometric uniqueness, and, more importantly, in some areas they may be impossible to obtain (e.g. in rural and arid areas). To overcome this problem we introduce an alternative approach that uses linear features such as roads and rivers for image mosaicking. Such features are identified and matched to their counterparts in overlapping imagery. Our matching approach uses critical points (e.g. breakpoints) of linear features and the information conveyed by them (e.g. local curvature values and distance metrics) to match two such features and orient the images in which they are depicted. In this manner we orient overlapping images by comparing breakpoint representations of complete or partial linear features depicted in them. By considering broader feature metrics (instead of single points) in our matching scheme we aim to eliminate the effect of erroneous point matches in image mosaicking. Our approach does not require prior approximate parameters, which are typically an essential requirement for successful convergence of point matching schemes. Furthermore, we show that large rotation variations about the z-axis may be recovered. With the acquired orientation parameters, image sequences are mosaicked. Experiments with synthetic aerial image sequences are included in this thesis to demonstrate the performance of our approach

Diagnosis of physical and biological controls on phytoplankton distribution in the Gulf of Maine-Georges Bank region

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 1999The linkage between physics and biology is studied by applying a one-dimensional model and a two-dimensional model to the Sargasso Sea and the Gulf of Maine- Georges Bank region, respectively. The first model investigates the annual cycles of production and the response of the annual cycles to external forcing. The computed seasonal cycles compare reasonably well with the data. The spring bloom occurs after the winter mixing weakens and before the establishment of the summer stratification. Sensitivity experiments are also carried out, which basically provide information of how the internal bio-chemical parameters affect the biological system. The second model investigates the effect of the circulation field on the distribution of phytoplankton, and the relative importance of physical circulation and biological sources by using a data assimilation approach. The model results reveal seasonal and geographic variations of phytoplankton concentration, which compare well with data. The results verify that the seasonal cycles of phytoplankton are controlled by both the biological source and the physical advection, which themselves are functions of space and time. The biological source and the physical advection basically counterbalance each other. Advection controls the tendency of the phytoplankton concentration more often in the coastal region of the western Gulf of Maine than on Georges Bank, due to the small magnitude of the biological source in the former region, although the advection flux divergences have greater magnitudes on Georges Bank than in the coastal region of the western Gulf of Maine. It is also suggested by the model results that the two separated populations in the coastal region of the western Gulf of Maine and on Georges Bank are self-sustaining

Diagnosis of physical and biological control over phytoplankton in the Gulf of Maine-Georges Bank region using an adjoint data assimilation approach

The linkage between physical and biological processes, particularly the effect of the circulation field on the distribution of phytoplankton, is studied by applying a two-dimensional model and an adjoint data assimilation approach to the Gulf of Maine-Georges Bank region. The model results, comparing well with observation data, reveal seasonal and geographic variations of phytoplankton concentration and verify that the seasonal cycles of phytoplankton are controlled by both biological sources and advection processes which are functions of space and time and counterbalance each other. Although advective flux divergences have greater magnitudes on Georges Bank than in the coastal region of the western Gulf of Maine, advection control over phytoplankton concentration is more significant in the coastal region of the western Gulf of Maine. The model results also suggest that the two separated populations in the coastal regions of the western Gulf of Maine and on Georges Bank are self-sustaining.Shandong Sheng (China) (Shandong Young and Middle-Aged Scientists Research Award, grant BS2011HZ021

Diagnosis of physical and biological controls on phytoplankton distribution in the Sargasso Sea

The linkage between physical and biological processes is studied by applying a one-dimensional physical-biological coupled model to the Sargasso Sea. The physical model is the Princeton Ocean Model and the biological model is a five-component system including phytoplankton, zooplankton, nitrate, ammonium, and detritus. The coupling between the physical and biological model is accomplished through vertical mixing which is parameterized by the level 2.5 Mellor and Yamada turbulence closure scheme. The coupled model investigates the annual cycle of ecosystem production and the response to external forcing, such as heat flux, wind stress, and surface salinity, and the relative importance of physical processes in affecting the ecosystem. Sensitivity experiments are also carried out, which provide information on how the model bio-chemical parameters affect the biological system. The computed seasonal cycles compare reasonably well with the observations of the Bermuda Atlantic Time-series Study (BATS). The spring bloom of phytoplankton occurs in March and April, right after the weakening of the winter mixing and before the establishment of the summer stratification. The bloom of zooplankton occurs about two weeks after the bloom of phytoplankton. The sensitivity experiments show that zooplankton is more sensitive to the variations of biochemical parameters than phytoplankton.Shandong Sheng (China) (Shandong Young Scientists Research Award, grant BS2011HZ021