NDLI: Semi-supervised hyperspectral image classification using a new (soft) sparse multinomial logistic regression model

Content Provider	IEEE Xplore Digital Library
Author	Jun Li Bioucas-Dias, J.M. Plaza, A.
Copyright Year	2011
Description	Author affiliation: Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, E-10071 Caceres, Spain (Plaza, A.) \|\| Instituto de Telecomunicações, Instituto Superior Técnico, TULisbon, 1900-118, Lisboa, Portugal (Jun Li; Bioucas-Dias, J.M.)
Abstract	In this work, we propose a new semi-supervised classification algorithm for remotely sensed hyperspectral images. The main contribution of this work is the development of new soft sparse multinomial logistic regression $(S^{2}MLR)$ model which exploits both hard and soft labels. In our terminology, these labels respectively correspond to labeled and unlabeled training samples. In order to obtain the soft labels, we use a recently proposed subspace-based MLR algorithm (MLRsub). The proposed semi-supervised algorithm represents an innovative contribution with regards to conventional semi-supervised learning algorithms that only assign hard labels to unlabeled samples. The effectiveness of our proposed method is evaluated via experiments with a widely used hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region in Indiana. Our results indicate that the proposed method provides state-of-the-art performance when compared to other methods.
Starting Page	1
Ending Page	4
File Size	183112
Page Count	4
File Format	PDF
ISBN	9781457722028
ISSN	21586268
e-ISBN	9781457722011
DOI	10.1109/WHISPERS.2011.6080879
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-06
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Unlabeled training samples Semi-supervised learning Soft labels Kernel Sparse multinomial logistic regression Hyperspectral imaging Logistics Hyperspectral image classification
Content Type	Text
Resource Type	Article

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