Fractal compression of satellite images
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Photonirvachak
J. Indian Soc. Remote Sens. (December 2008) 36:299–311
RESEARCH ARTICLE
Fractal Compression of Satellite Images Jayanta Kumar Ghosh . Ankur Singh
Received: 22 October 2007 / Accepted: 25 September 2008
Keywords Fractal compression . IRS satellite images
Abstract Fractal geometry provides a means for describing and analysing the complexity of various features present in digital images. In this paper, characteristics of Fractal based compression of satellite data have been tested for Indian Remote Sensing (IRS) images (of different bands and resolution). The fidelity and efficiency of the algorithm and its relationship with spatial complexity of images is also evaluated. Results obtained from fractal compression have been compared with
popularly used compression methods such as JPEG 2000, WinRar. The effect of bands and pixel resolution on the compression rate has also been examined. The results from this study show that the fractal based compression method provides higher compression rate while maintaining the information content of RS images to a great extent than that of JPEG. This paper also asserts that information loss due to fractal compression is minimal. It may be concluded that fractal technique has many potential advantages for compression of satellite images.
J.K. Ghosh1 ( ) . Ankur Singh2 1 Assistant Professor, Geomatics Engineering Group, Civil Engineering Department, Indian Institute of Technology Roorkee, Uttarakhand – 247667, India 2 Software Engineer, SAP Labs India Ltd., RMZ NXT, 2B/2C, Sonnenahalli Village, Mahadevapura, East Taluk, Bangalore – 560066, India
Introduction
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Rapid development of remote sensing technology leads to the generation of huge quantities of satellite data. These data, in the form of multispectral, multiresolution, multisensor, have become important sources for geographic information but require considerable space for storage. For example, a scene of LANDSAT TM image has 2,340 (rows) × 3,240 (columns) × 7 (bands) pixels. Each pixel requires 8
300
bits to represent 256 gray levels. So it needs 424,569,600 bits of storage. For the images taken from IKONOS, the storage requirements are even larger because of high radiometric resolution (it uses 11 bits to represent 2048 gray levels). On the other hand, the complexity and density of remote sensing data have also become much higher. For example, IRS PAN, LISS and other sensors provide data of about 1 terabyte per day. The range of spatial resolutions from these satellites varies from meters to tens of kilometers, and with a wide range of wavelengths from visible to microwave, as well as a more frequently repeated coverage. Moreover, hyper spectral remote sensing satellites use a large numbers of bands with resolution of tens of meters, and produces hundreds of gigabytes of data sets for even small regional coverage. Similarly, terrestrial and video images are also generating quite a lot of data. Evidently, the amount of spatial and spectral satellite data is enormous. Desp
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