Geospatial integration and fusion techniques for environmental monitoring and security

Geographic Information (GI) is more and more seen as an integral part of the modem information and communication society. Improved methods for data access and integration have accelerated this process. Remotely sensed image data has increasingly been used to generate the necessary current, accurate, and synoptic information for the GI databases. For applications such as environmental monitoring, large-scale mapping or urban information systems, remotely sensed data of very high spatial resolution are required. Traditionally, aerial photography was used as standard imaging input. The advent of the new satellites with a resolution of better than I in and digital airborne scanner sensors with a high geometric fidelity and spatial resolution in the centimeter range, however, challenge the analog airphoto techniques. These new digital airborne and spacebome high-resolution sensors offer an advanced potential for environmental mapping and monitoring. Almost all of the new generation satellite and aircraft sensors, however, provide high-resolution information only in their panchromatic mode whereas the multispectral images are of lower spatial resolution. The ratios between high-resolution panchromatic and low-resolution multispectral images vary between 1:2 and 1:8 (or even higher if different sensors are involved). Consequently, appropriate techniques have been developed to merge the high-resolution panchromatic information into the multispectral datasets. These techniques are usually referred to as pansharpening or data fusion. The methods can be classified into three levels: pixel-level (iconic) fusion, feature-level (symbolic) fusion and decision-level fusion. We will present exemplary case studies for each of these levels. Fusion examples will include: A new color preserving iconic adaptive pansharpening technique that also works with multitemporal/multisensor data Segment-based fusion of high-resolution orthoimages and hyperspectral datasets for urban material classification Decision-based integration of panchromatic high-resolution data with multispectral images for the identification of settlement areas Rapid image enhancement merging GIS and multispectral satellite data.