Geodatabase Centric Orthoimage Production Using Arcgis Image Server

In traditional image production workflows, imagery is processed in multiple steps, with the imagery being sampled multiple times, and being read and written to disk multiple times. This reduces image quality and production efficiency. The linear workflows also are susceptible to bottlenecks when a single parameter in the workflow is not available or needs to be changed. ArcGIS Image server enables a geodatabase centric workflow by which the parameters and models for processing imagery are stored within a database and the imagery product is generated on demand as required directly from the base imagery. This methodology provide a number of advantages. For example, it enables serving of dynamic image services that can be updated as revised parameters become available. It enables the creation of graded products that change over time with the revised parameters and enabling improvements in quality assurance processes. The same image services can also be used to generate imagery products in the form of caches or the traditional tiled images that are required by most orthoimage mapping projects. The quality of the imagery is superior due to reduced sampling of the images. By optimizing the processing and reducing disk access, the production system is very efficient and scalable, enabling high production rates. 1. TRADITIONAL ORTHOIMAGE PRODUCTION Traditional orthoimage production workflows, result in imagery being processed in multiple steps. Typically, an image will go through the following steps: radiometric correction, pansharpening, aerial triangulation, orthorectification, mosaicking, reprojection, then product generation. Depending on the software used, the order of these steps may change, but typically each step is performed separately with imagery being read, sampled, or enhanced and then written to another location. With each radiometric or geometric enhancement some information is lost, resulting in the final image quality being non-optimum. Even processes, such as mosaicking multiple images together, result in unnecessary sampling if pixels of all the input and output images are not aligned. Additionally, if the sampling of the input and output imagery is a very similar resolution (which is often the case when re-projecting) then aliasing artefacts can also become apparent especially in imagery covering areas that are near featureless, but have good textures such as over water or gravel desert. In some areas the imagery appears to have slightly higher contrast than in other areas. These artefacts are caused by pixels in some regions being sampled to be close to the average of four neighbouring pixels (which reduces the local contrast), while in other areas the pixel is the nearly solely derived from a single input pixel (maintaining the local contrast). Although different sampling methods may be defined to reduce these effects, they are generally at the cost of accuracy. To reduce the data volumes some workflows apply lossy compression methods to the intermediate products, which further increases the creation of artifacts and degrades quality. Such orthoimage generation workflows where the imagery is read and written multiple times are also not truly scalable. Traditionally, such workflows are scaled using technologies such as CORBA, enabling distributed processing over multiple machines. The processing gains are quickly mitigated by network and harddisk bottlenecks, which are caused by the multiple reads and writes of large image data volumes to disks saturating the available bandwidth as well as fragmenting the disks. 1.1 Why do we need a geodatabase centric approach? Linear process workflows can also cause substantial bottlenecks in production. A delay in one step will stop subsequent steps. For example, if the accurate orthoimage product is not created due to the non-availability of a terrain model, the color balancing cannot be performed, since color balancing in such workflows are dependant on the orthoimages. As projects become larger, the chances of one part of the process being delayed increases which in turn increase the delay and risks for the complete project. Such production workflows can be considered a set of independent tasks and not all tasks need to be performed necessarily in a fixed order. Each of the production steps can be considered to consist of two components: The determination of process parameters, and/or the application of these parameters on the imagery. The process of aerial triangulation is a typical example of parameter determination with no pixel processing being applied. Orthorectification is a process that utilizes the parameters of orientation and a terrain model to apply a pixel process. During the complete image production workflow, there are many parameters that affect the resulting product. These include parameters of orientation, radiometric enhancement, pan-sharpening, terrain models, and mosaic seamlines. These production stages are actually quite loosely coupled. For example, the parameters of pan-sharpening have no effect on aerial triangulation. The determination of color balancing parameters for imagery is often dependant on the orthoimages, but does not require accurate orthoimages nor