A remotely sensed data separation method with neural networks

In this paper, we investigated a data processing method with independent component analysis (ICA) and proposed a pattern classification system for remote sensing data based on neural network theory. From independent component analysis, training data for each pattern are converted to an independent data set regardless of observation sensors. Using the BP algorithm, the layered neural network is trained such that the training pattern can be classified within a level. The experiments on TM data show that this approach produces excellent classification results compared with conventional statistical approaches (the Bayesian and distance methods etc)