基于SSAE深度学习特征表示的高光谱遥感图像分类方法
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摘要
针对遥感图像中高光谱数据的分类问题,提出一种基于堆叠稀疏自动编码器(SSAE)深度学习特征表示的高光谱遥感图像分类方法;首先,将光谱数据样本进行预处理和归一化;然后,将其输入到SSAE中进行特征表示学习,并通过网格搜索来获得最优网络参数,以此获得有效的特征表示;最后通过支持向量机(SVM)分类器对输入图像特征进行分类,最终实现遥感图像中像素的分类;在两个标准数据集上的实验结果表明,该方法能够实现准确的高光谱地物分类。
Aiming at the classification problem of hyperspectral data in remote sensing images,a hyperspectral remote sensing image classification method based on the deep learning feature representation by stacked sparse auto-encoder(SSAE)is proposed.First,the spectral data samples are pre-processed and normalized.Then,it is input into the SSAE for feature representation learning,and the grid search is used to obtain the optimal network parameters,thereby obtaining a valid feature representation.Finally,the input image features are classified by the support vector machine(SVM)classifier,and finally the pixels in the remote sensing image are classified.Experimental results on two standard datasets show that this method can achieve accurate hyperspectral landmark classification.
Aiming at the classification problem of hyperspectral data in remote sensing images,a hyperspectral remote sensing image classification method based on the deep learning feature representation by stacked sparse auto-encoder(SSAE)is proposed.First,the spectral data samples are pre-processed and normalized.Then,it is input into the SSAE for feature representation learning,and the grid search is used to obtain the optimal network parameters,thereby obtaining a valid feature representation.Finally,the input image features are classified by the support vector machine(SVM)classifier,and finally the pixels in the remote sensing image are classified.Experimental results on two standard datasets show that this method can achieve accurate hyperspectral landmark classification.
引文
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[15]侯榜焕,姚敏立,贾维敏,等.面向高光谱图像分类的空谱判别分析[J].光学精密工程,2018,46(2):306-315.
[2]樊利恒,吕俊伟,邓江生.基于分类器集成的高光谱遥感图像分类方法[J].光学学报,2014,34(9):91-101.
[3]Liu L,Li C F,Lei Y M,et al.Feature extraction for hyperspectral remote sensing image using weighted PCA-ICA[J].Arabian Journal of Geosciences,2017,10(14):307.
[4]Ren J,Zabalza J,Marshall S,et al.Effective feature extraction and data reduction with hyperspectral imaging in remote sensing[J].IEEE Signal Processing Magazine,2014,31(4):149-154.
[5]王珂,顾行发,余涛,等.基于频谱相似性的高光谱遥感图像分类方法[J].中国科学:技术科学,2013(4):407-416.
[6]Chen Y,Jiang H,Li C,et al.Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks[J].IEEE Transactions on Geoscience&Remote Sensing,2016,54(10):6232-6251.
[7]戴晓爱,郭守恒,任淯,等.基于堆栈式稀疏自编码器的高光谱影像分类[J].电子科技大学学报,2016,45(3):382-386.
[8]Samadzadegan F.Spectral–spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder[J].Journal of Applied Remote Sensing,2017,11(4):132-138.
[9]Su Y,Marinoni A,Li J,et al.Nonnegative sparse autoencoder for robust endmember extraction from remotely sensed hyperspectral images[A].2017IEEE International Geoscience and Remote Sensing Symposium[C].IEEE,2017:205-208.
[10]Han X,Zhong Y,Zhang L.Spatial-Spectral Unsupervised Convolutional Sparse Auto-Encoder Classifier for Hyperspectral Imagery[J].Photogrammetric Engineering&Remote Sensing,2017,83(3):195-206.
[11]刘建军,吴泽彬,韦志辉,等.基于空间相关性约束稀疏表示的高光谱图像分类[J].电子与信息学报,2012,34(11):2666-2671.
[12]Zhang L,Lu Y.Comparison of auto-encoders with different sparsity regularizers[A].International Joint Conference on Neural Networks[C].IEEE,2015:1-5.
[13]Cao G M,Ding X Q,Gong H L.Study on Image Recognition Based on Stacked Sparse Auto-encoder[A].International Conference on Electronics,Electrical Engineering and Information Science[C].2017:152-156.
[14]吴一全,周杨,龙云淋.基于自适应参数支持向量机的高光谱遥感图像小目标检测[J].光学学报,2015,35(9):322-331.
[15]侯榜焕,姚敏立,贾维敏,等.面向高光谱图像分类的空谱判别分析[J].光学精密工程,2018,46(2):306-315.