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.
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机译:本文提出了一种用于偏振合成孔径雷达(SAR)图像的混合作物分类器。特征集包括跨度图像,H / A /α分解和基于灰度共现矩阵(GLCM)的纹理特征。然后,通过主成分分析(PCA)来减少特征。最后,通过自适应混沌粒子群优化(ACPSO)构造并训练了两层前向神经网络(NN)。使用K折交叉验证来增强生成。 Flevoland站点上的实验结果证明了ACPSO优于反向传播(BP),自适应BP(ABP),动量BP(MBP),粒子群优化(PSO)和弹性反向传播(RPROP)方法。此外,每个像素的计算时间仅为1.08×10 -7 sup> s。
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