WAVELET APPLICATION IN COMPRESSION OF A REMOTE SENSED IMAGE

Present scenario gives importance to precision image representing information about remote sensing, medicine, seismic etc. which are more importance to decision makers and researchers. These precision data are of very high resolution in nature in order to give optimal information which in turn occupies higher level of disk space. The main practical problem in transmitting these information are that primarily it requires a higher bandwidth, greater time involved in reception etc. There are numerous data compression algorithms but the primary concern is that the compression should be Lossless before and after reception. This paper employs the mathematical tool ‘Wavelet’ for the precision data compression and decompression. The advantage of compression permits the decision maker to process and send back after modifications, in the base image data. Wavelets find a greater application in obtaining lossless compression technique. Wavelet decomposes the data into various frequency components that helps to study and analyze each component with a resolution matched to its scale. Key Word : Wavelet, Bandwidth, Classifier, Lossless, Lossy, Quantized