Hyperspectral image classifications and feature selection
Mahesh Pal, Department of Civil Engineering, National Institute of Technology, Kurukshetra, India
Abstract
This chapter discusses the use of a modified radial basis function (RBF) network for the classification of hyperspectral data. The use of RBF networks has already been reported in remote sensing literature with different approaches to determine the weights between the hidden and output layers. The RBF network proposed in this chapter uses Cholesky decomposition and the least mean square error method to determine the weights between the hidden layer and the output layers. The availability of the large number of features in hyperspectral data represents a challenge due to data redundancy ...
Get Hyperspectral Remote Sensing now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
