NEURAL-NETWORK CLASSIFICATION OF REMOTE-SENSING DATA

Neural nets offer the potential to classify data based upon a rapid match to overall patterns using previously calculated weighting factors, rather than point-by-point comparisons involving algorithmic logic applied to individual data values. Analytical tasks thus are greatly reduced. This paper describes an example of the use of artificial neural networks to classify remotely sensed data, determining that the networks can provide a useful level of categorization. The addition of texture data improves general discrimination ability of the network but diminishes its ability to distinguish between specific types of vegetation. Procedures for optimizing net design were successfully identified. This study validates use of neural networks by application to a larger data set than has been employed previously, and extends previous findings in several ways: (1) It documents a method for designing and training networks which may be used to achieve within-class discrimination for a given data set at a level comparable to human classification. (2) It incorporates texture analysis of the input data. At the expense of extra computation, this permits analysis of the spatial relationships among pixels, instead of being limited to considering the pixels individually. (3) It provides a working prototype system which may be used for generalized standard classification of other land image data sets created by multispectral scanner.