基于分层稀疏表示特征学习的高光谱图像分类研究

李铁; 孙劲光; 张新君; 王星

doi:doi:10.3788/lop53.091001

激光与光电子学进展, 2016, 53 (9): 091001, 网络出版: 2016-09-14

基于分层稀疏表示特征学习的高光谱图像分类研究下载： 654次

Research of Hyperspectral Image Classification Based on Hierarchical Sparse Representation Feature Learning

论文大纲

李铁 ^1,*孙劲光 ¹张新君 ²王星 ¹

作者单位

¹ 辽宁工程技术大学电子与信息工程学院, 辽宁葫芦岛 125105

² 大连理工大学计算机科学与技术学院, 辽宁大连 116024

遥感高光谱图像分类特征学习稀疏表示 remote sensing hyperspectral image classification feature learning sparse representation

AI 词云图 AI一句话精读 AI短摘要

注：本部分内容由 AI 自动生成，请您知悉。

摘要

提出一种基于分层稀疏表示特征学习的方法即分层判别特征学习算法对高光谱图像进行分类，在两层的分层结构中用空间金字塔匹配模型在每层的稀疏编码上用最大池化方法学习得到判别特征，用分层判别特征学习得到的特征表示对于分类更稳健、判别性更好。在两个高光谱数据集上评价该方法，结果表明，该方法具有更好的分类精度。

Abstract

A method of classification based on hierarchical sparse representation feature learning as hierarchical discriminative feature learning algorithm is developed for hyperspectral image classification. The spatial-pyramid-matching model is used, and the sparse codes learned from the discriminative features are obtained by max pooling in each layer of the two-layer hierarchical structure. The representation of features achieved by the proposed method are more robust and discriminative for the classification. The proposed method is evaluated on two hyperspectral datasets, and the results show that the proposed method has good classification accuracy.

参考文献

[1] 沈毅, 张敏, 张淼. 基于互信息波段选择和经验模态分解的高精度高光谱数据分类[J]. 激光与光电子学进展, 2011, 48(9): 091001.

Shen Yi, Zhang Min, Zhang Miao. Mutual information bands selection and empirical mode decomposition based support vector machine for hyperspectral data high accuracy classification[J]. Laser & Optoelectronics Progress, 2011, 48(9): 091001.

[2] Zhang H, Li J, Huang Y, et al. A nonlocal weighted joint sparse representation classification method for hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2056-2065.

[3] 孙鹏, 高卫, 孙奕帆. 几种高光谱目标探测算法性能的分析比较[J]. 激光与光电子学进展, 2015, 52(9): 092801.

Sun Peng, Gao Wei, Sun Yifan. Analysis and comparison of several target detectors for hyperspectral imaging[J]. Laser & Optoelectronics Progress, 2015, 52(9): 092801.

[4] 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): 0928001.

Wu Yiquan, Zhou Yang, Long Yunlin. Small target detection in hyperspectral remote sensing image based on adaptive parameter SVM[J]. Acta Optica Sinica, 2015, 35(9): 0928001.

[5] 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): 0910002.

Fan Liheng, Lü Junwei, Deng Jiangsheng. Classification of hyperspectral remote images based on bands grouping and classification ensembles[J]. Acta Optica Sinica, 2014, 34(9): 0910002.

[6] Fang L, Li S, Kang X, et al. Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(12): 7738-7749.

[7] Falco N, Benediktsson J A, Bruzzone L. A study on the effectiveness of different independent component analysis algorithms for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2183-2199.

[8] Song B, Li J, Dalla Mura M, et al. Remotely sensed image classification using sparse representations of morphological attribute profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 5122-5136.

[9] Pan Z, Glennie C, Legleiter C, et al. Estimation of water depths and turbidity from hyperspectral imagery using support vector regression[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(10): 2165-2169.

[10] Zou Z, Shi Z. Hierarchical suppression method for hyperspectral target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 330-342.

[11] Ni D, Ma H. Hyperspectral image classification via sparse code histogram[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(9): 1843-1847.

[12] Kang X, Li S, Fang L, et al. Intrinsic image decomposition for feature extraction of hyperspectral images[J]. IEEE Transactions on Geoscience Remote Sensing, 2015, 53(4): 2241-2253.

[13] Ghamisi P, Benediktsson J A. Feature selection based on hybridization of genetic algorithm and particle swarm optimization[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(2): 309-313.

[14] Zhou Y, Peng J, Chen C L P. Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2): 1082-1095.

[15] Kang X, Li S, Fang L, et al. Extended random walker-based classification of hyperspectral images[J]. IEEE Transactions on Geoscience Remote Sensing, 2015, 53(1): 144-153.

李铁, 孙劲光, 张新君, 王星. 基于分层稀疏表示特征学习的高光谱图像分类研究[J]. 激光与光电子学进展, 2016, 53(9): 091001. Li Tie, Sun Jinguang, Zhang Xinjun, Wang Xing. Research of Hyperspectral Image Classification Based on Hierarchical Sparse Representation Feature Learning[J]. Laser & Optoelectronics Progress, 2016, 53(9): 091001.

基于分层稀疏表示特征学习的高光谱图像分类研究下载： 654次

关于本站 Cookie 的使用提示

全站搜索

基于分层稀疏表示特征学习的高光谱图像分类研究 下载： 654次

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索

基于分层稀疏表示特征学习的高光谱图像分类研究下载： 654次