Nonlinear Principal Component Analysis for the Radiometric Inversion of Atmospheric Profiles by Using Neural Networks

F. Del Frate, and G. Schiavon

Abstract

A new neural network algorithm for the inversion of radiometric data to retrieve atmospheric profiles of temperature and vapor has been developed. The potentiality of the neural networks has been exploited not only for inversion purposes but also for data feature extraction and dimensionality reduction. In its complete form, the algorithm uses a neural network architecture consisting of three stages: 1) the input stage reduces the dimension of the input vector; 2) the middle stage performs the mapping from the reduced input vector to the reduced output vector; 3) the third stage brings the output of the middle stage to the desired actual dimension. The effectiveness of the algorithm has been evaluated comparing its performance to that obtainable with more traditional linear techniques.

Index Terms—Atmospheric profiling, microwave radiometry, neural networks

© 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE.