Hyperspectral image classification and application based on relevance vector machine. [J]. Journal of Remote Sensing 14(6):1273-1284(2010) DOI: 10.11834/jrs.20100615.
Hyperspectral image classification and application based on relevance vector machine
The relevance vector machine(RVM) is used to process the hyperspectral image in this paper to estimate the classifiers precisely in the high dimensional space with limited training samples.The detail of RVM is firstly discussed based on the sparse Bayesian theory.Then four multi-class strategies are analyzed
including One-vs-All(OAA)
One-vs-One(OAO) and two direct multi-class strategies.In the experiments
the multi-class strategies are compared and RVM is further compared with several classical classifiers
including the support vector machine(SVM).The experiments show that two direct multi-class strategies occupy too much memory space with low efficiency.OAA has the highest precision
but is low in efficiency.OAO is the best in efficiency and the precision approximates to OAA.Compared with SVM
RVM is low in precision
but sparser than SVM.The sparse property is important when the test set is large
which makes RVM suitable for classifying the large-scale hyperspectral image.
Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology
Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Central South University of Forestry & Technology
Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Central South University of Forestry & Technology