Fractal Image Compression Applied to Remote Sensing

Remote sensing images contain huge amount of geographical information and reflect the complexity of geographical features and spatial structures. Fractal geometry provides a means of describing and analyzing the complexity of different geographical features in remotely sensed images. It also provides a more powerful tool to compress the remote sensing data than traditional methods. In this study, based on fractal concepts, compression and decompression algorithms were developed and applied to Landsat TM images of eight study areas with different land cover types; the fidelity and efficiency of the algorithms and their relationship with the spatial complexity of the images were evaluated. Three research hypotheses were tested and the fractal compression was compared with commonly used compression methods. The effects of spatial complexity and pixel resolution on the compression rate were also examined. Keywords—Fractal image compression, Iterated Function Systems (IFS), Partitioned Iterated Function Systems, Remote sensing.