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Fusion of statistical and learnt features for SAR images classification

机译：统计和学习特征融合用于SAR图像分类

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摘要

Deep-learning-based methods often suffer from insufficient training samples when they are directly used in the task of Synthetical Aperture Radar (SAR) images classification, which in turn leads to poor performance. To alleviate this problem, this paper presents a feature-fused approach, in which several statistical features of SAR images are extracted and integrated into the first layer of a typical Convolutional Neural Networks (CNNs). Since SAR images exhibit evidently statistical properties, those statistical features, which are characterized by non-linearity and cannot be adaptively learnt by CNNs in the first layer, can be thought of as prior knowledge to facilitate performance enhancement. Experiments conducted on real TerraSAR-X dataset demonstrate the effectiveness of the proposed method, and the classification accuracy is improved by about 2%.
机译：当基于深度学习的方法直接用于合成孔径雷达（SAR）图像分类任务时，经常会遇到训练样本不足的情况，从而导致性能下降。为了缓解这个问题，本文提出了一种特征融合方法，其中SAR图像的一些统计特征被提取并集成到典型的卷积神经网络（CNN）的第一层中。由于SAR图像具有明显的统计特性，因此那些具有非线性特征且第一层CNN无法自适应学习的统计特性可以被视为有助于提高性能的先验知识。在真实TerraSAR-X数据集上进行的实验证明了该方法的有效性，分类精度提高了约2％。

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