Wavelet frame-based feature extraction technique for improving classification accuracy

Abstract

Classification of textures in remotelysensed 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.

Contourlet-Based Multiband Image Fusion for Improving Classification Accuracy in IRS LISS III Images

“…The multitemporal multiband satellite sensor images used in this study of Madurai city in Tamil Nadu are shown [12] in Fig. 1a.…”

Performance Analysis of K-Means Clustering For Remotely Sensed Images

“…The multitemporal multiband satellite sensor image [14] used in this study was Madurai city in Tamilnadu shown in Fig.1 Madurai is the second largest city in Tamilnadu and is one of the oldest cities in India. It is one of the 12 heritage cities in India and it is situated between longitudes.…”