Wavelet frame-based feature extraction technique for improving classification accuracy

Classification of textures in remotely-sensed data has received considerable attention during the past decades. One difficulty of texture analysis in the past was lack of adequate tools to characterize different scales of textures effectively. Recent space-frequency analytical tools like the wavelet transform can effectively characterize the coupling of different scales in texture and helps to overcome the difficulty. This paper presents a wavelet-based texture classification technique that was applied to a Multi-Spectral Scanner (MSS) image of Madurai City, Tamil Nadu, India The feature extraction stage of the technique uses Lemarie-Battle orthogonal wavelets to derive a texture feature vector and this vector is input to a fuzzy-c means classifier, an unsupervised classification procedure. Four indices (user’s accuracy, producer’s accuracy, overall accuracy and Kappa co-efficient) are used to assess the accuracy of the classified data. The experiment results show that the performance of the presented technique is superior to the classical techniques.