Nonlinear principal component analysis for the radiometric inversion of atmospheric profiles by using neural networks

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