This paper proposes a hybrid classifier for polarimetric SAR images. The feature sets consist of span image, the H/A/α decomposition, and the GLCM-based texture features. Then, a probabilistic neural network (PNN) was adopted for classification, and a novel algorithm proposed to enhance its performance. Principle component analysis (PCA) was chosen to reduce feature dimensions, random division to reduce the number of neurons, and Brent’s search (BS) to find the optimal bias values. The results on San Francisco and Flevoland sites are compared to that using a 3-layer BPNN to demonstrate the validity of our algorithm in terms of confusion matrix and overall accuracy. In addition, the importance of each improvement of the algorithm was proven.
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机译：本文提出了一种用于极化SAR图像的混合分类器。特征集包括跨度图像，H / A /α分解和基于GLCM的纹理特征。然后，采用概率神经网络（PNN）进行分类，并提出了一种新的算法来提高其性能。选择主成分分析（PCA）以减小特征尺寸，选择随机划分以减少神经元数量，并选择Brent搜索（BS）来找到最佳偏差值。将旧金山和Flevoland站点上的结果与使用3层BPNN的结果进行比较，以证明我们的算法在混淆矩阵和总体准确性方面的有效性。另外，证明了算法的每个改进的重要性。
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