Abstract
Feature extraction using polarimetric synthetic aperture radar (PolSAR) images is of great interest in SAR classification, no matter if it is applied in an unsupervised approach or a supervised approach. In the supervised classification framework, a major group of methods is based on machine learning. Various machine learning methods have been investigated for PolSAR image classification, including neural network (NN), support vector machine (SVM), and so on. Recently, representation-based classifications have gained increasing attention in hyperspectral imagery, such as the newly-proposed sparse-representation classification (SRC) and nearest-regularized subspace (NRS). These classifiers provide excellent performance that is comparable to or even better than the classic SVM for remotely-sensed image processing. However, rare studies have been found to extend this representation-based NRS classification into PolSAR images. By the use of the NRS approach, a polarimetric feature vector-based PolSAR image classification method is proposed in this paper. The polarimetric SAR feature vector is constructed by the components of different target decomposition algorithms for each pixel, including those scattering components of Freeman, Huynen, Krogager, Yamaguchi decomposition, as well as the eigenvalues, eigenvectors and their consequential parameters such as entropy, anisotropy and mean scattering angle. Furthermore, because all these representation-based methods were originally designed to be pixel-wise classifiers, which only consider the separate pixel signature while ignoring the spatial-contextual information, the Markov random field (MRF) model is also introduced in our scheme. MRF can provide a basis for modeling contextual constraints. Two AIRSAR data in the Flevoland area are used to validate the proposed classification scheme. Experimental results demonstrate that the proposed method can reach an accuracy of around 99 % for both AIRSAR data by randomly selecting 300 pixels of each class as the training samples. Under the condition that the training data ratio is more than 4 % , it has better performance than the SVM, SVM-MRF and NRS methods.
Highlights
Due to the active microwave imaging characteristics of fine resolution, day-and-night and weather-independence, Synthetic Aperture Radar (SAR) has been widely used for Earth remote sensing for more than 30 years, and it has come to play a significant role in geographical survey, climate change research, environment and Earth system monitoring, multi-dimensional mapping and other applications [1]
Results and Analysis of Flevoland I Data nearest-regularized subspace (NRS) is implemented on the AIRSAR dataset in Flevoland I firstly, at the same time comparing with the one combined with Markov random field (MRF), i.e., NRS-MRF, classic support vector machine (SVM) and the combination of SVM-MRF
After applying the combined NRS-MRF method, taking the spatial information into consideration, the classification accuracy is increased to 99.23%; while, the classification results of SVM and SVM-MRF are 87.10% and 94.33%, respectively
Summary
Due to the active microwave imaging characteristics of fine resolution, day-and-night and weather-independence, Synthetic Aperture Radar (SAR) has been widely used for Earth remote sensing for more than 30 years, and it has come to play a significant role in geographical survey, climate change research, environment and Earth system monitoring, multi-dimensional mapping and other applications [1]. The polarization characteristics are highly related to the properties of the target, such as structure, size, posture, and so on. In the past few decades, many polarimetric SAR (PolSAR) systems (AIRSAR, ESAR, Pi-SAR, SAR580-Convair, ALOS2, RADARSAT-2, TerraSAR-X, etc.) have been developed and applied in land cover classification [2,3], soil moisture inversion [4,5,6,7], tomography retrieval [8], target detection [9,10], and so on. Size, dielectric and moisture characteristics of the target affect PolSAR data, the data are sensitive to the geometry of the building, branching structure and shape of the leaves [11,12]. The PolSAR data classification has attracted extensive attention and become a hot topic in PolSAR applications [2,3,13,14,15,16,17]
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