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Position: Home > Articles > Remote sensing image classification based on deep learning features and support vector machine Journal of Forestry Engineering 2019 (2) 119-125

基于深度学习特征和支持向量机的遥感图像分类

作  者：
业巧林;许等平;张冬
单  位：
南京林业大学信息科学技术学院;国家林业局调查规划设计院
关键词：
深度学习;遥感图像分类;支持向量机;卷积神经网络;分类精度
摘  要：
随着遥感图像采集技术的迅速发展,传统的遥感图像处理方法已经不能满足当前实际的生产需要。近年来,深度学习模型的流行为遥感图像分类问题的解决提供了新的途径。因此,为了进一步提升遥感图像的分类精度,笔者提出了一种基于深度学习特征和支持向量机(support vector machine,SVM)的遥感图像分类模型。首先,针对深度学习模型需要海量训练数据的特点,运用旋转、剪裁等方法对原始的遥感图像进行数据扩增;然后,将扩增数据按照种类随机地分为训练集和验证集两部分测试集,并使用训练集和验证集训练改进的针对遥感图像分类问题的卷积神经网络(convolutional neural network,CNN)中的参数,进而在训练好的CNN模型上提取第一部分测试集的深度学习特征;最后,使用第一部分测试集的深度特征训练多分类SVM,并对第二部分测试集图像进行分类验证。实验采用NWPU-RESISC45公共数据集对本研究模型精度进行验证,与现有的遥感图像分类方法相比较,实验结果表明,提出模型的总体分类精度有明显提升,从而验证了方法的有效性和实用性。
译  名：
Remote sensing image classification based on deep learning features and support vector machine
作  者：
YE Qiaolin;XU Dengping;ZHANG Dong;College of Information Science and Technology,Nanjing Forestry University;Academy of Forest Inventory and Planning,State Forestry Administration;
关键词：
deep learning;;remote sensing image classification;;support vector machine;;convolution neural network;;classification accuracy
摘  要：
To detect and monitor the feature changes in a specific area,remote sensing can measure the reflected and emitted radiation signals at a long distance from that area. Remote sensing can be utilized in many fields,such as,natural resource detection,agricultural crops and forestry management,mineral exploration,buildings and bridges monitoring,land usage and coverage mapping,and global weather change research. Recently,the remote sensing has been widely applied in the forestry industry economy. With the remarkable progress of the remote sensing image acquisition technology,the traditional remote sensing image classification methods cannot meet the actual needs of the forestry industry economy. In recent years,the emergence and prevalence of the deep learning models have provided many novel ideas for the classification and recognition of remote sensing images. To further improve the classification accuracy of remote sensing images,in this paper,a method of the remote sensing image classification based on the combination of the deep learning feature and support vector machine( SVM) model was proposed. Firstly,because the characteristic of the deep learning models needs the massive training data,the rotation and clipping for augmenting the number of the original remote sensing images were operated. Then,the augmented data were divided into the training set,twofold testing sets and the validation set. The parameters of the convolutional neural network( CNN) were trained by the training set and the validation set,and the features of the first testing samples were extracted based on the trained CNN model. Finally,the deep features of the second testing set were classified using the trained SVM with the first testing data. The method was validated by the NWPU-RESISC45 public data set. Compared with the existing classification methods of remote sensing image,experimental results showed that the overall classification accuracy of the proposed method was improved,which validated the validity and practicability of the proposed method.

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