Polarimetric Synthetic Aperture Radar (PolSAR) has been proven to recognize and classify various ground objects, andmultitemporal fully PolSAR can acquire many scattering features to improve the accuracy of recognition and classification. However, the
decomposed scattering features with high dimensionality can cause serious problems of“curse of dimensionality.”This paper proposes a
multitemporal PolSAR scattering feature dimension reduction method based on Stacked Sparse AutoEncoder (S-SAE) to effectively reduce
the dimensionality of high-dimensional scattering features. The proposed method firstly decomposes the PolarSAR data to obtain high-
dimensional scattering features and adopts S-SAE to reduce the dimensionality of the acquired multidimensional features. For the S-SAE
construction, unsupervised layer-by-layer greedy training is performed to optimize the main parameters. Combined with a sigmoid classifier,
the parameters of S-SAE are finely tuned through supervised training to achieve effective dimension reduction of high-dimensional features.
The reduced low-dimensional features are taken as the input of Support Vector Machine (SVM) and Convolutional Neural Network (CNN)
classifiers to evaluate the performance of feature dimension reduction. The performance of the proposed method is validated on two datasets
of multitemporal simulated and real Sentinel-1 data. Experimental results show that the S-SAE method with two hidden layers achieves the
best performance of feature dimension reduction. Compared with traditional Locally Linear Embedding (LLE) and Principal Component
Analysis (PCA) dimension reduction methods, the overall classification accuracy of S-SAE for the SVM classifier is raised by at least 9%
and 14%. The overall classification accuracy of S-SAE for the CNN classifier is at least 7% and 9% higher than that of LLE and PCA,
respectively.
修改评论