Improved POLSAR Image Classification by the Use of Multi-Feature Combination

Remote Sensing

Lei Deng²  Ya-nan Yan²  Cuizhen Wang¹  Gonzalo Pajares Martinsanz³ 

[1] Department of Geography, University of South Carolina, Columbia, SC 29208, USA; E-Mail:;College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China; E-Mail:;College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China; E-Mail

关键词: urban classification;    polarimetric SAR;    time-frequency decomposition;    decision tree;   

DOI  :  10.3390/rs70404157

来源: mdpi

【 摘 要 】

Polarimetric SAR (POLSAR) provides a rich set of information about objects on land surfaces. However, not all information works on land surface classification. This study proposes a new, integrated algorithm for optimal urban classification using POLSAR data. Both polarimetric decomposition and time-frequency (TF) decomposition were used to mine the hidden information of objects in POLSAR data, which was then applied in the C5.0 decision tree algorithm for optimal feature selection and classification. Using a NASA/JPL AIRSAR POLSAR scene as an example, the overall accuracy and kappa coefficient of the proposed method reached 91.17% and 0.90 in the L-band, much higher than those achieved by the commonly applied Wishart supervised classification that were 45.65% and 0.41. Meantime, the overall accuracy of the proposed method performed well in both C- and P-bands. Polarimetric decomposition and TF decomposition all proved useful in the process. TF information played a great role in delineation between urban/built-up areas and vegetation. Three polarimetric features (entropy, Shannon entropy, T11 Coherency Matrix element) and one TF feature (HH intensity of coherence) were found most helpful in urban areas classification. This study indicates that the integrated use of polarimetric decomposition and TF decomposition of POLSAR data may provide improved feature extraction in heterogeneous urban areas.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.