Spatial Metadata for Remote Sensing Imagery

Mining the petabyte and growing archive of remotely sensed images to obtain the necessary information for land cover change studies becomes more difficult as more imagery is obtained and stored at various locations by government agencies or private companies. The increasing importance of networking with the requirement to move data sets between different servers and clients makes the data volume problem particularly acute. Mitigating this problem requires using data efficiently, that is, using data at the appropriate scale and resolution to adequately characterize phenomena, thus providing accurate answers to the questions being asked. This requires a thorough understanding of the effects of adjusting image resolution to match the resolution of images from other sources or supporting data for biophysical or urban growth models. This paper summarizes a NASA Intelligent Systems-funded project that is examining the use of fractals and geostatistical techniques as aids to image classification and segmentation, as indicators of the effects of image processing techniques such as rectification, rescaling, ratioing and classification, as indicators for change detection, and as metadata for mining and selecting appropriate imagery for global change studies. This project will provide benchmarked indices of image complexity that complement existing metadata schemas and will facilitate image retrieval and analysis. This will streamline global change investigations by quickly identifying the proper source, lineage, general image content, scale, and resolution of imagery suited to an analysis of anthropogenic alterations in land cover, thus allowing researchers to concentrate on the underlying physical processes and potential consequences of these changes.