Compression and Analysis of Very Large Imagery Data Sets Using Spatial Statistics

As remote sensing instruments evolve, the size of imagery data sets derived from remote sensing continues to increase. Several satellites currently offer resolution of 1 meter per pixel or better. At this resolution, even a small geographic area leads to a very large data set; 1 square mile, for example, is represented by approximately 2.6 x 10^6 pixels. Many sensors are now multispectral or even hyperspectral, increasing the size of the data set by up to 10^2. Processing images for classification or mapping purposes thus poses an increasing computational challenge. This paper describes the use of spatial statistics to compress the size of large 1-meter imagery data sets. The images were taken over locations in the United States using a CAMIS (Computerized Airborne Multispectral Imaging System) instrument flown in an airplane and registered by trained image analysts. Models of spatial variation are first computed on an entire image, then on subsampled sets of the image. Parameters of the models are used to compress the original image. In some cases it is possible to compress data several orders of magnitude without substantially degrading results of subsequent analysis