Fast adaptive wavelet for remote sensing image compression

Remote sensing images are hard to achieve highcompression ratio because of their rich texture. By analyzing theinfluence of wavelet properties on image compression, this paperproposes wavelet construction rules and builds a new biorthogonalwavelet construction model with parameters. The model parameters areoptimized by using genetic algorithm and adopting energy compaction asthe optimization object function. In addition, in order to resolve thecomputation complexity problem of online construction, according to theimage classification rule proposed in this paper we construct waveletsfor different classes of images and implement the fast adaptive waveletselection algorithm (FAWS). Experimental results show wavelet bases ofFAWS gain better compression performance than Daubechies9/7.