To investigate effects of pixel to pixel correlation on classification performance for TM data, some experiments have been conducted. One of the results shows that pixel to pixel correlation in homogeneous fields of TM data is larger than that of the simultaneously acquired MSS data. This implies that it is more important to take into account the pixel to pixel correlation for TM classification rather than MSS classification. To overcome the effect due to the correlation, the following methods have been investigated. Based on the auto-regessive model, modify the Gaussian statistical parameters for the correlated image to those for uncorrelated stochastic process. An example using an agricultural TM data shows a slight improvement in terms of the weighted mean percentage of correct classification. Through the investigation of the correlation structures, more realistic model for TM data has been clarified.
This paper describes a symbols used in large scale method of subroutinization mapping by personal computer. First, the legend of these maps were analyzed and a table of the various symbols applied in different large scale maps was made out according to the line or area of symbols, such as, circle, triangle and etcetera. Then the classification of symbol elements was tested by analyzing “similar type symbols”. Regarding to the symbol subroutinization process, the standards for fixed-form symbols used in legends of large scale maps were set and the subroutine for most symbols were produced. The result shows that this method can process the map symbols more efficiently.