Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine–based data fusion system

Behnaz Bigdeli; Parham Pahlavani

doi:10.1117/1.JRS.11.016007

12 January 2017 Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine–based data fusion system

Behnaz Bigdeli, Parham Pahlavani

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 016007 (January 2017). https://doi.org/10.1117/1.JRS.11.016007

Abstract

Interpretation of synthetic aperture radar (SAR) data processing is difficult because the geometry and spectral range of SAR are different from optical imagery. Consequently, SAR imaging can be a complementary data to multispectral (MS) optical remote sensing techniques because it does not depend on solar illumination and weather conditions. This study presents a multisensor fusion of SAR and MS data based on the use of classification and regression tree (CART) and support vector machine (SVM) through a decision fusion system. First, different feature extraction strategies were applied on SAR and MS data to produce more spectral and textural information. To overcome the redundancy and correlation between features, an intrinsic dimension estimation method based on noise-whitened Harsanyi, Farrand, and Chang determines the proper dimension of the features. Then, principal component analysis and independent component analysis were utilized on stacked feature space of two data. Afterward, SVM and CART classified each reduced feature space. Finally, a fusion strategy was utilized to fuse the classification results. To show the effectiveness of the proposed methodology, single classification on each data was compared to the obtained results. A coregistered Radarsat-2 and WorldView-2 data set from San Francisco, USA, was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with optical sensor based on the proposed methodology improve the classification results for most of the classes. The proposed fusion method provided approximately 93.24% and 95.44% for two different areas of the data.

Citation Download Citation

Behnaz Bigdeli and Parham Pahlavani "Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine–based data fusion system," Journal of Applied Remote Sensing 11(1), 016007 (12 January 2017). https://doi.org/10.1117/1.JRS.11.016007

Received: 8 June 2016; Accepted: 20 December 2016; Published: 12 January 2017

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