CONJUGATE-GRADIENT NEURAL NETWORKS IN CLASSIFICATION OF MULTISOURCE AND VERY-HIGH-DIMENSIONAL REMOTE-SENSING DATA

Application of neural networks to classification of remote sensing dat a is discussed. Conventional two-layer backpropagation is found to giv e good results in classification of remote sensing data but is not eff icient in training. A more efficient variant, based on conjugate-gradi ent optimization, is used for classification of multisource remote sen sing and geographic data and very-high-dimensional data. The conjugate -gradient neural networks give excellent performance in classification of multisource data but do not compare as well with statistical metho ds in classification of very-high-dimensional data.