基于深度学习特征融合的遥感图像场景分类应用
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中北大学

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Remote Sensing Image Scene Classification Application Based on Deep Learning Feature Fusion
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中北大学

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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    针对传统手工特征方法无法有效提取整体图像深层信息,提出基于深度学习特征融合的场景分类新方法。首先利用灰度共生矩阵(GLCM)和局部二值模式(LBP)提取具有相关空间特性的纹理特征和局部纹理特征的浅层信息;其次通过基于AlexNet迁移学习网络提取图像的深层信息,在去除最后一层全连接层的同时加入一层256维的全连接层作为特征输出;将两种特征进行自适应融合;最终输入到网格搜索算法优化的支持向量机(GS-SVM)中对遥感图像进行场景分类识别。在公开数据集UC Merced的21类目标数据和RSSCN7的7类目标数据的实验结果表明,五次实验的平均准确率分别达94.77%和93.79%。实验结果与参考文献其它方法对分类精度进行对比,表明该方法有效提升遥感图像场景的分类精度。

    Abstract:

    In view of the traditional manual feature method, which can not effectively extract the overall image deep information, a new method of scene classification based on deep learning feature fusion is proposed. Firstly, the grayscale symbiotic matrix (GLCM) and local two-value pattern (LBP) are used to extract the shallow information of texture features and local texture features with relevant spatial characteristics, and secondly, the deep information of images is extracted by the AlexNet migration learning network, and a 256-dimensional full-connect layer is added as feature output while the last layer of full connection layer is removed, and the two features are adaptively integrated The remote sensing images are classified and identified in the support vector machine (GS-SVM) optimized by the grid search algorithm. The experimental results of the 21 types of target data of the public dataset UC Merced and the 7 types of target data of RSSCN7 show that the average accuracy rate of the five experiments was 94.77% and93.79%, respectively. The experimental results are compared with other methods in the references and the classification accuracy under the same data conditions, which shows that the proposed method can effectively improve the classification accuracy of remote sensing image scenes.

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历史
  • 收稿日期:2022-03-22
  • 最后修改日期:2022-05-05
  • 录用日期:2022-05-09
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