A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration
A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration
复制标题
基于空间统计建模和特征重标定的SAR场景分类深度学习新算法
DOI：
10.3390/s19112479
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发表时间：
2019-05
期刊：
影响因子：
3.9
通讯作者：
Jia Zhiwei
中科院分区：
文献类型：
--
作者：
Chen Lifu;Cui Xianliang;Li Zhenhong;Yuan Zhihui;Xing Jin;Xing Xuemin;Jia Zhiwei
合成孔径雷达（SAR）场景分类是一项具有挑战性但应用广泛的研究课题，其中深度学习因其具有层次特征学习能力而发挥着关键作用。本文提出了一种新的场景分类框架——基于多尺度空间特征的特征再标定网络（FRN-MSF），以实现基于sar的场景分类的高精度。首先构造了一个多尺度全向高斯导数滤波器（MSOGDF）。然后通过加权MSOGDF、灰度梯度共现矩阵（GLGCM）和Gabor变换生成SAR场景的多尺度空间特征（MSF）；通过特征再校准网络（FRN）对这些特征进行处理，学习高级特征。该网络集成了深度可分离卷积（DSC）、压缩激励（SE）块和卷积神经网络（CNN）。最后，通过Softmax函数对这些学习到的特征进行分类。对4种不同波段和分辨率的系统获得的11种SAR场景进行了训练和测试，平均准确率为98.18%。为了验证FRN-MSF的通用性，从另外两幅大尺度高分三号和TerraSAR-X图像中采样的五种类型的SAR场景进行了分类评估。5种类型的平均准确率达到94.56%；而对于相同的5种类型，前者测试的11种场景的平均准确率为96%。该方法具有较高的分类精度，在不丧失一般性的前提下，有望用于SAR场景分类。
Synthetic Aperture Radar (SAR) scene classification is challenging but widely applied, in which deep learning can play a pivotal role because of its hierarchical feature learning ability. In the paper, we propose a new scene classification framework, named Feature Recalibration Network with Multi-scale Spatial Features (FRN-MSF), to achieve high accuracy in SAR-based scene classification. First, a Multi-Scale Omnidirectional Gaussian Derivative Filter (MSOGDF) is constructed. Then, Multi-scale Spatial Features (MSF) of SAR scenes are generated by weighting MSOGDF, a Gray Level Gradient Co-occurrence Matrix (GLGCM) and Gabor transformation. These features were processed by the Feature Recalibration Network (FRN) to learn high-level features. In the network, the Depthwise Separable Convolution (DSC), Squeeze-and-Excitation (SE) Block and Convolution Neural Network (CNN) are integrated. Finally, these learned features will be classified by the Softmax function. Eleven types of SAR scenes obtained from four systems combining different bands and resolutions were trained and tested, and a mean accuracy of 98.18% was obtained. To validate the generality of FRN-MSF, five types of SAR scenes sampled from two additional large-scale Gaofen-3 and TerraSAR-X images were evaluated for classification. The mean accuracy of the five types reached 94.56%; while the mean accuracy for the same five types of the former tested 11 types of scene was 96%. The high accuracy indicates that the FRN-MSF is promising for SAR scene classification without losing generality.
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