Fusion of texture and wavelet features of PALSAR image using LDA and PCA for land cover classification. (2nd October 2017)

• Record Type: Journal Article
• Authors: Gupta, Shruti; Singh, Dharmendra; Kumar, Sandeep
• Is Part Of: International journal of image and data fusion. Volume 8:Number 4(2017)
• Page Start: 354
• Page End: 374
• Publication Date: 2017-10-02
• Subjects: SAR (Synthetic Aperture Radar) -- texture -- features -- ANOVA (Analysis of Variance) -- PCA (Principal Component Analysis) -- LDA (Linear Discriminant Analysis); Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2017.1355337
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: Land cover classification is yet a challenging task due to complex landscapes, less a priori knowledge and complicated data sets. Researchers are utilising scattering-based model, polarimetric-based model and empirical model for classification purpose on SAR (Synthetic Aperture Radar) data, but achieving good classification accuracy requires more consideration. Most of the work reported has been performed by either utilising only single polarisation channel or on the composite image, due to which the significance of each channel remains unidentified. Previously, SAR feature-based land cover characterisation has shown favourable results, by examining the use of one or two forms of features and not much attempt has been dedicated to the simultaneous incorporation of multiple types of texture and wavelet features in a manner that preserves all the significant segregation information for land cover classification. Therefore, in this paper, different statistical, textural and wavelet features were analysed on three individual polarimetric channels (HH, HV and VV). For enhancing the discrimination capability, principal component analysis and linear discriminant analysis have been employed and the classification information attained from the three polarimetric channels was fused for accounting the implication of each of them. Proposed methodology is screening decent classification accuracy of 80.83% with kappa coefficient 0.7289, with less complexity and easy implementation.