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

Lei Deng,Cuizhen Wang,Ya-Nan Yan

doi:10.3390/rs70404157

Abstract

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.

Highlights

Terrain and land-use classification is an important component of synthetic aperture radar (SAR)image application
SAR data in early years were often collected at a single frequency and pre-determined polarization (H or V), which precluded the separation and mapping of terrain classes due to limited information obtained by these systems [1]
A group of methods have been proposed for classifying Polarimetric SAR (POLSAR) imagery, which can be divided into three schemes

Summary

Introduction

Terrain and land-use classification is an important component of synthetic aperture radar (SAR)image application. The decomposed polarimetric parameters are related to physical properties of natural media and help in identifying terrain classes Example classifiers in this scheme include the Entropy/Anisotropy/Alpha [7], Freeman 3-component decomposition [8], and Yamaguchi 4-component decomposition [9]. The second classification scheme incorporates statistical data such as the polarimetric covariance matrix and the distance between an unknown pixel and a clustering center in feature space [10,11]. These statistical measures have been commonly applied in regular supervised or unsupervised (e.g., ISODATA)

Methods

Discussion

Conclusion