Data fusion with artificial neural networks for classification of earth surface from microwave satellite measurements

Abstract

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven Special Sensor Microwave Imager (SSMI) multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both horizontal and vertical polarizations (only vertical at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including nonscattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (a) preprocessing to extract feature vectors and enhance separability among detected classes; (b) preliminary classification of Earth surface patterns using two separate and parallel-acting classifiers: back-propagation neural network and binary decision tree classifiers; and (c) data fusion of results from preliminary classifiers to obtain the optimal performance in over-all classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4% are used as the training set and 96% as the testing set. After training, this classification system is able to bring up the detection accuracy to 94% compared with 88% for back-propagation artificial neural networks and 80% for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).