School of Information Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2011;11(5):4721-43. doi: 10.3390/s110504721. Epub 2011 May 2.
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.
本文提出了一种用于极偏合成孔径雷达（SAR）图像的混合作物分类器。特征集由跨度图像、H/A/α 分解和基于灰度共生矩阵（GLCM）的纹理特征组成。然后，通过主成分分析（PCA）对特征进行降维。最后，构建了一个具有两个隐藏层的前馈神经网络（NN），并通过自适应混沌粒子群优化（ACPSO）进行训练。采用 K 折交叉验证来增强生成。在 Flevoland 站点上的实验结果表明，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)s。
