Abstract
This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.
Highlights
The classification of different objects, as well as different terrain characteristics, with single channel monopolarisation synthetic aperture radar (SAR) images can carry a significant amount of error, even when operating after multilooking [1]
The Wishart maximum likelihood (WML) method has often been used for polarimetric SAR (PolSAR) classification [3]
It does not take explicitly into consideration the phase information contained within polarimetric data, which plays a direct role in the characterization of a broad range of scattering processes
Summary
The classification of different objects, as well as different terrain characteristics, with single channel monopolarisation SAR images can carry a significant amount of error, even when operating after multilooking [1]. The Wishart maximum likelihood (WML) method has often been used for PolSAR classification [3]. It does not take explicitly into consideration the phase information contained within polarimetric data, which plays a direct role in the characterization of a broad range of scattering processes. PSO is chosen to find the optimal parameters of the neural network. By randomly initializing the algorithm with candidate solutions, the PSO successfully leads to a global optimum [25]. This is achieved by an iterative procedure based on the processes of movement and intelligence in an evolutionary system.
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