Hyperspectral image classification based on local and structural feature with sparse multinomial logistic regression

doi:10.13474/j.cnki.11-2246.2019.0178

Abstract

Abstract:

Sparse multinomial logistic regression only uses spectral information in image classification.Therefore,the classification effect is poor. In this paper,a hyperspectral image classification method based on local and structural feature with sparse multinomial logistic regression is proposed. Firstly, weighted mean filter and extended multi attribute profiles are used to extract local and structural features of the original hyperspectral images. Then the weighted average feature level fusion is carried out to obtain more unique pixel feat ures.Finally,the fusion results are classified by sparse multinomial logistic regression.The results show that the proposed method has well classification effect and robustness.

Key words: hyperspectral image, feature fusion, weighted mean filter, extended multi-attribute profiles, sparse multinomial logistic regression

CLC Number:

P237

SHEN Yuzhen, GUAN Yunlan, YANG Lu, LIU Chengcheng, YAN Xiaofang. Hyperspectral image classification based on local and structural feature with sparse multinomial logistic regression[J]. 测绘通报, 2019, 0(6): 24-28.

References

[1] 孙艳丽.联合丰度信息与空谱特征的高光谱影像分类研究[D].北京:中国科学院遥感与数字地球研究所,2017.
[2] 孙乐.空谱联合先验的高光谱图像解混与分类方法[D].南京:南京理工大学,2014.
[3] 李彩虹,赵祎霏.一种高光谱图像的半监督分类方法[J].测绘通报,2018(2):46-49.
[4] 谷雨,徐英,郭宝峰.融合空谱特征和集成超限学习机的高光谱图像分类[J].测绘学报,2018,47(9):1238-1249.
[5] CAO Faxian, YANG Zhijing, REN Jinchang, et al. Extreme sparse multionmial logistic regression:a fast and roubust framework for hyperspectral image classification[J].Remote Sensing,2017,9(12):1255.
[6] KRISHNAPURAM B, CARIN L, FIGUEIREDO M A T, et al. Sparse multinomial logistic regression:fast algorithms and generalization bounds[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(6):957-968.
[7] BOHNING D. Multinomial logistic-regression algorithm[J].Annals of the Institute of Stati stical Mathematics,1992,44(1):197-200.
[8] ZHOU Yicong, PENG Jiangtao, 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.
[9] 鲍蕊.光谱和空间特征联合的高光谱遥感影像多分类器集成方法[D].南京:南京大学,2016.
[10] 王启聪.高光谱图像分类的GPU并行优化研究[D].南京:南京理工大学,2015.
[11] LI J, MARPU P R, PLAZA A, et al. Generalized composite kernelframework for hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2013,51(9):4816-4829.
[12] LI J, HUANG X, GAMBA P, et al. Multiple feature learning for hyperspectral image classfication[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(3):1592-1606.
[13] LI J, BIOUCAS-DIAS J M, PLAZA A. Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression[J].IEEE Geoscience Remote Sensing Letters,2013,10(2):318-322.
[14] BAO R, XIA J, MURA M D, et al. Combining morphological attribute profiles via an ensemble method for hyperspectral image classification[J]. IEEE Geoscience Remote Sensing Letters,2016,13(3):359-363.
[15] MURA M D, BENEDIKTSSON J A, WASKE B, et al. Morphological attribute profiles for the analysis of very high resolution images[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(10):3747-3762.